London – 13 September, 2012 – Emailvision Holdings Ltd., the leader in marketing automation and customer intelligence, today announced it has acquired PredictiveIntent Ltd., a pioneer in behavioural targeting and predictive analytics technology. With this acquisition, Emailvision is extending its customer intelligence offering to help marketers deliver more relevant and personalised marketing experiences on their websites and email marketing campaigns.

PredictiveIntent’s SaaS solution enables marketers to transform their websites into a dynamic, personalised sales environment. Its patent-pending predictive analytics technology presents relevant content to every site visitor in real time. With intelligence about product preferences from PredictiveIntent, marketers can automatically target subscribers with highly personalised email campaigns.

Emailvision’s Founder and CEO Nick Heys said of the acquisition, “Behavioural targeting is transforming the world of relationship marketing. Digital marketers who use PredictiveIntent see double-digit percent increases to conversion rates and sales. We see tremendous business potential for marketers who integrate predictive analytics into their online relationship marketing programmes.”

Neil Hamilton, CEO and Co-Founder of PredictiveIntent, comments, “PredictiveIntent’s vision has been to increase our customers’ revenues through enabling real time personalised digital experiences. Our customers are integrating our service into both their websites and email marketing, the most prolific being joint users of PredictiveIntent and Emailvision services. The synergy between our goals, customers and culture was uncanny. We’re delighted to be part of a truly global marketing software company and look forward to expanding our technology offering and reach.”

As Senior Customer Intelligence Analyst Joe Stanhope writes in Forrester Research Inc.’s June 2012 report entitled ‘Behavioral Targeting Powers Customized Content and Increased Conversion’, “Today’s sophisticated Internet consumers have high expectations for the websites they visit. As the Internet grows increasingly noisy and competitive, consumers demand well-designed, relevant experiences.

Behavioural targeting empowers marketers to meet these growing demands by delivering content and promotions that are tuned to the behavior and characteristics of website visitors.”

– Ends –

About Emailvision

The smartest relationship marketing campaigns are powered by Emailvision. With more than 13 years of research and development, Emailvision is delivering SaaS solutions for the next-generation of relationship marketers. The organization provides pioneering software for marketing automation and customer intelligence. Every month Emailvision’s marketing cloud platform delivers 600,000 campaigns for 3,300 clients. Emailvision is located in 22 countries around the world.

About PredictiveIntent

Founded in 2009, PredictiveIntent is a leading provider of true behavioral personalization technology for digital businesses. The company’s mission is simple; to make advanced internet personalization available to all businesses who employ digital marketing. Recognized in 2010 by The Guardian as a leading UK innovator, our patent-pending technology enables customers to create personalized website experiences that increase customer loyalty, conversions and revenue.

For more information, please contact:

Berkeley PR
Denis Davies / Claire Hutchison
Email: Emailvision@berkeleypr.co.uk
Telephone: +44 1189 090 909

And this phenomenon can be replicated easily online – retailers can place low value “no brainer” products or product banners on the checkout page, aiming to increase average order values. This blog post reviews two different on how you can use the chocolate-at-the-checkout trick on your store to increase your AOV.

1. Use merchandising banners to sell “no brainers”

For stores which sell a niche range of products with a “basic”, “must-have”, “required” product, merchandising banners require minimal effort but produce good results. Placed underneath the main basket area, these banners should be bright and attention-grabbing but promote a product which won’t impact the sale of the product(s) already in the basket.

Lovehoney.com do this well – in fact, their head of online is where I first heard the term “Twix” in ecommerce. For the avoidance of doubt, Lovehoney isn’t a PI client.

The “Twix” banner image is eyecatching and has a clear call-to-action, an enticing offer and is a definite “no-brainer” product for their market Clicking the image puts the product into the basket, without redirecting to any other page – overall, this approach has been well thought through to encourage a click and designed to reduce any mental barriers which may arise (but could be improved by showing some “social proof” – perhaps the number of people who have taken up the offer today?).

2. Offer cross-sell, low-value product recommendations

Another approach to increasing AOV is to use product recommendations to entice an add-to-basket. These often take the form of “You May Also Like” or “People Who Bought The Items In Your Basket Also Bought” recommendations, but retailers are beginning to move away from this basic sourcing method into behavioural suggestions.

PredictiveIntent customer Hawkin’s Bazaar does this well. By only offering two product suggestions they are reducing the options available so as to not distract customers from their original purchase.

The “Have you thought about…” title encourages the customer to pay more attention to the suggestions, and hovering over a suggestion brings up an add-to-cart button and a quantity box – useful for a site where many purchases may be in multiples. Their suggestion sourcing “logic” also means it’s likely the suggestions will be “no brainer” low value customers which encourage quick add-to-basket’s.

Remember – not everyone likes Mars Bar’s…

The behaviour given for free by your visitor’s is extremely valuable, and when thought through properly can drive massive increases in revenues and conversion rates. When combining behavioural personalisation and either approach above, you can ensure you’re catering for each customer’s preferences whilst meeting your own business goals.

In the first example, Lovehoney use the same banner image and offer, regardless of the products in the basket. By looking at the category and attributes of the product in the basket, various “no brainer” products can be sourced according to different rules and criteria to ensure relevancy – and can also take into account individual visitor preferences such as allergies, sizes and so on.

What do you think?

Have you seen any great examples of Twix approaches from online retailers? What would you do differently?

Leave a comment below or tweet us @TweetIntent.

For a great checklist on getting prepared for Christmas, take a look at this Econsultancy post from 201 that still holds true today. However, read on to learn how adding more data can make your holiday revenues magical.

It’s all about the data.

Data (and content) is key. Your product pages will be optimised with relevant and unique descriptions; images will be zoomable, clear and on-brand, and important product specifications will be neatly listed.

But it’s not all about data that the consumer can see and use.

Hidden attributes, sleeping data.

One of my favourite ways to shop at Christmas is to see what products retailers think is suitable for the person I’m buying for.

Shopping for Him, for Her, For Kids are just a few examples of retailers using category navigation to drive people towards more relevant products that fulfill their current shopping need. But retailers don’t think about how that data can be used in personalisation technology to provide more relevant recommendations and content.

Use it, or lose revenue.

With personalisation technology, the bigger the dataset, the more relevant the recommendations can be. Just one extra attribute (“for him”, for example) can have a massive impact on the content and products a visitor is presented with – not only can it be used in real-time to suggest other “for him” products right there and then, but it can be used to produce more relevant “people who bought this also bought” recommendations, it can be remembered to be used in a year’s time, and so on.

The opportunities and rewards to be had by adding more product data is immense. Netflix have a team of film experts, watching movies and tagging them with appropriate plot lines, sub-stories and theme, in order to improve the quality of the recommendations. If a retailer could add behavioural, emotional and mental state information to products, this could provide supercharged, totally relevant recommendations – as well as great insight into your customer base that can be used elsewhere.

Ready, Get Set, Go!

Because our PersonalMerchant technology is deeply integrated into your ecommerce platform, we are aware of all of your products and their attribute data – including stock levels (meaning we can never suggest a product that is unavailable).

And thanks to quick-install extensions and a simple to understand API, you can be up and running in days – contact us today to find out more about how we can work together.

You’re also quite advanced with your email marketing – segmenting based on purchase categories or demographics, trigger emails and so on.

How can retailers connect the two together?

Personal Product Recommendations in Email

By pushing personalised  content into their email marketing platform, retailers can make use of the unique and valuable behaviour data given freely by customers and those known to the retailer to supercharge return on investment and clickthrough rates. 14% of US retailers view personal recommendations in email as the most successful channel for ROI.

Why?

Retailers are moving away from a spray’n'pray email marketing approach – having found that blast email approaches don’t work that well, the use of segmentation is growing (42% of retailers use segmentation in their campaigns).

But recipients are usually segmented by demographics, location or other criteria that places them in a large group with other people. And retailers don’t like to create segments too small, asit would be ineffecient to create hundreds of emails for each campaign.

But by connecting personalisation technology to email marketing platforms, retailers can send segment of one emails to each and every recipient, taking into account their colour and brand preferences, shoe size or any other implicit or explicitly declared information. Usually, this would just be product recommendations however could create a completely personalised email, from product suggestions right down to text, images and call to actions.

How?

Through the wonder of APIs! These allow our core software to tell your email platform which product data is needed for each recipient. By creating a set of logic for a campaign (e.g. show relevant cross-sells, make sure they’re own brand and are around the same price) our new LPS calculates suggestions for each recipient, linking together onsite behaviour with an email address or using more crowd-based or historical data for those recipients we’ve never seen on the site.

Once we’ve sent the email platform the data (in the form of a product image URL, a product page deep link, the price and the product name), retailers can then design or use existing email templates by inserting merge tags, which are replaced for each email that goes out with the relevant information.

What?

Not only does your recipient receive a personalised and relevant email, but you can realise higher clickthrough rates and higher generated revenue from each campaign, whilst using different logic configurations (such as minimum price filters, product margin filters, etc) to consciously ensure your business goals are met.

Next Steps

To find out more about how PredictiveIntent’s personalisation technology can personalise digital touchpoints for the individual, contact us today.

These are called “sorting cues” and they guide visitors towards highlight content. However, these few words can have a massive impact on the success metrics delivered by personalised product recommendations, including derived revenue and clickthrough rates.

Hidden Meanings

Whilst you might think each phrase accurately describes the “logic” behind the recommendations, usually titles are interchangable and bear no relation to how the products are decided.

However, to your users each one is subtly different.

Social Proof

Sorting cues such as “Popular Today”, “Best Sellers” and “People Who Bought This Also Bought” might be used to promote social proof. Social proof is often used where users may be uncertain or need to reinforce an impending decision.

“Popular Today”, “Best Sellers” and other similar sorting cues are often used on homepages, but any social proof derived from this is really only useful to returning visitors who are seeing the product they are returning to buy there.

Using “People Who Bought This Also Bought” on product pages can detract the visitor from their original goal by conveying “cross-sell” opportunities and presenting more choice – similar to what we found looking at product recommendations when scrollers were used.

Rather than push users away from the product they are looking for, we advocate using “up-sell” logic to suggest similar but more expensive product. Here, the cue “People Who Looked At This Also Looked At”, or “People Who Looked At This [Eventually] Bought” is more appropriate as it suggests both the right “logic” and uses social proof to push visitors towards more expensive products, increasing average order value.

Authority

This is a favourite of ours – using authority and expert-ship to drive users towards recommended products. However, they can sometimes present obstacles to visitors who are yet to declare an intention in a product category or preferences towards colours, styles, brands, etc.

“Editor’s Picks” are very handy for fashion sites that create or curate content, driving great results for sites which have a loyal following who have ‘bought in’ to the brand and their story. This sorting cue can be used anywhere on a site, but are especially great for directing new or returning visitors towards products they wouldn’t usually be interested in – usually on the homepage. “We Like” is another interchangable sorting cue that conveys similar meaning but may be more suited for non-fashion sites such as electronics or gadgets.

“Complete The Outfit”, “Get The Look” and the like are useful for fashion retailers for increasing average order values, however are typically seen on product pages to promote cross-sell products. We would advise using similar sorting cues on a cart page, suggesting cross-sell products usually with a maximum price, to act as “no-brainers”.

Scarcity

A little used on-site psychological strategy, scarcity can be used well in email marketing. Using sorting cues such as “Last Chance” or “Get Them Before They’re Gone”  work by directing users to believe products are more important because of a lack of availability, and also leads back to the social proof tactic described above.

Importance

“Recommended For You” is a great way to direct users towards content they know is personalised and just for them. Best used on “My Account” pages, these instill importance and curation – an interesting approach would be to use a Pinterest-style layout with recommended products and blog posts, pushing the boundaries of blended content further.

Test, Test and Test.

It’s 2012 – every retailer should be testing every part of their ecommerce site looking to increase conversions and revenue – testing sorting cues should be no different.

Multivariate testing will allow many combinations of sorting cues in different areas all over the site to find the combination which presents the best results. By split testing the “logic” behind the recommendations, retailers using PersonalMerchant from PredictiveIntent can continuously optimise their personalisation strategies with help from our expert support and client success team.

What’s Next?

Here are some psychology in ecommerce resources we think you’d like.

• A blog post from us on how “You May Also Like” isn’t fit for the modern consumer.
• Psychology of Social Commerce infographic on GetElastic
• Guido Jansen’s excellent resource on Psychology strategies for Ecommerce

If you’d like to find out more about how PredictiveIntent can help you increase revenues through relevance, get in touch today.

Please share if you found this article useful!

You can find the slidedeck below. If you’d like to find out more about how PredictiveIntent can help your B2B business increase online sales, get in touch with us today. 

Nominated by online retailers in the US, the eTail Rising Star award celebrates companies that have made an impact on the ecommerce industry, providing value, performance and excellence throuh services for online retail.

After being shortlisted for Internet Technology of the Year at the BT Retail Week Technology Awards, 2012 is an award-winning year for PredictiveIntent!

The video is below!

Find out how PredictiveIntent can help you double your revenue – contact us today.

PredictiveIntent offer IntentPredictionServer technology that not only provides relevance across channels, but acts as a single view of the customer – linking customer data and behaviour to enable better automated relevance and capture data for analysis.

If you’re interested in how we can help multichannel retailers close the gap between behaviour and practice, get in touch today.

And yes, I agree – personalisation not only drives revenue, but it creates more opportunities for conversion and can mine the implicit data created by visitors for use again and elsewhere.

But I’m not so sure about Pinterest and iPads.

Hmm, Pinteresting…

Pinterest, after a fairly slow start in 2010, has suddenly rocketed into the ecommerce stratosphere, claiming to drive more traffic and conversions than any other social service. If you’re  not familiar with the saviour of ecommerce (hey, nice rock, is that sandstone?), Pinterest (in my opinion) is nothing more than an image only scrapbook used by lower, borderline-middle class American women to create an online self that loves cosy fashion, retro jewellery, quirky Instagrammed fruit bowl arrangements and Hampton’s beach house-inspired furniture.

So for ecommerce, Pinterest is apparently of some use. People post photos, other people re-pin, people click through, and people buy these items.

I don’t want to criticise Pinterest – from what I’ve read and heard, it seems like it’s doing a good job. Instead, I want to criticise the authors choice of words.

Let’s move onto the iPad.

iPotatoes?

And what a great, tweetworthy statement they make. “90 percent of all mobile revenue is generated via iPads.” 

Seriously? Firstly, if true, then why all the fuss about mobile “showrooming” in stores? Secondly, I can see some value from iPad visitors (possibly having more disposable income, etc) but I can’t believe that.

The author calls the iPad the “Ideal “Lean-Back” E-Commerce Device”: great for browsing on the sofa, etc. And I can’t fault that – sitting on the sofa watching TV in the evening with my iPad is now second nature whilst my laptop sits lonely on the study desk.

But the consumer value of the iPad is not “it makes online shopping whilst sitting on the sofa easier”. The iPad, to consumers, is an entertainment content consuming device. I think that any shopping done on the iPad – through apps or through optimised websites – is added revenue; instead it’s simply revenue that has been stolen from PCs.

I think mobile traffic from smartphones does have different uses and needs which can be met by apps and specially optimised websites. Tablet territory, however, is more complicated. Is tablet traffic stealing and adding minimal revenue from PC traffic? For some, perhaps no. For most, possibly.

Twitter Knows All

I put the question to Twitter. And got this reply.

@TweetIntent hmmm if they expand it out to Relevance, Ease of Discovery, Convenience instead maybe…

— Dan Coleman (@DanielMColeman) June 6, 2012

And it was exactly what I’m looking for.

Ecommerce is not accelerating simply because of personalization, Pinterest and the iPad. Ecommerce is growing fast because of better relevance, easier discovery and more convenience.

Relevance

Relevance, in this context, is not just about personalisation. It’s about creating more relevant experiences in a number of ways:

• More relevant product recommendations and content experiences
• More relevant inbound and above the line marketing
• Better buying to appeal to wider tastes and keep on top of trends

Discovery

Typically, products are discovered in a limited range of ways: search, browsing onsite, instore, PR, content, social. But Pinterest has nailed the social product discovery mechanism, but it’s still not that clever.

Imagine if Pinterest knew you liked blue dresses and started showing matching shoes?

Discovery still has a long mountain to climb, and I think content curation is the way to go. Either owned, paid for or unpaid curation byblogs, newspapers, magazines and social spaces.

Convenience

Imagine you’re watching TV, and you see an ad with a really cool pair of shoes. Before the iPad, you’d have to pick up your heavy laptop or walk over to your desktop computer. Even with smartphones, you’d have to squint at the relatively small screen.

The iPad isn’t a driver of ecommerce. Convenience, and easier access to the information people want, is. The iPad is the perfect device for off-the-cuff commerce, but I doubt it’s ability to create new revenue.

Surely other  leaps enabling convenience are more important: better delivery, next-day delivery and free delivery being among the most important.

Concluding

Perhaps the author had a headline brief to use alliteration (“the Three P’s: Personalization, Pinterest and iPad (well, iPad almost starts with a P)”) and get some buzzwords in there.

Or perhaps the article is just an overblown opinion piece based on heresay and thinly-veiled stats from (surprise, surprise!) vendors.

Or, I could be wrong! Pinterest could be driving traffic to create £million’s of revenue and the iPad could be adding £million’s of extra revenue. But I do know that personalisation – more relevant experiences – is the ecommerce revenue enabler to focus on for growing, stabile growth.

What do you think?

Have I got it wrong? At least agree with me that relevance, ease of discovery and convenience are better word choices! Leave your thoughts in the comments.

For example, one retailer wants to increase revenue from their highest margin product category. So, for each visitor to the site we look at their preferences and in the right context, use these implicitly given details to suggest the most relevant items from the high margin category. And it works – revenue is up by 34%.

In another example, one of our larger UK customers is targeting increased average order values. So all product up-sells are at least 10% more expensive; the first half of a cross-sell suggestion block has an unlimited maximum price filter whilst the second half of  is limited to £50 – providing a mixed range of relevant suggestions whilst attempting to sell more expensive products.

With more options, retailers can create a unique personalisation strategy that suits them and their goals. Infinitely configurable  options, plus expert help and training (for those who want to be more involved), allow retailers to do this with ease, using all of the merchandising knowledge already in their business.

Segmenting behaviour by country, for example, or creating different logic to power  certain categories, all helps retailers create consistent merchandising experiences across their multichannel properties, whether it’s instore, on mobile sites or online stores.

If you’d like to find out how PersonalMerchant can help you create an advanced, customised personalisation strategy for your business – get in touch today.

Last year we carried out an A/B split test with a client using a vertical scroller, displaying 3 products at any one time on the left-hand sidebar, whilst requesting 12 suggestions in total – enough for three “scrolls”. Clickthrough to a suggestion was just 1.38%, generating 1.6% of their revenue.

In the version without a scroller, which requested and displayed only 4 products, clickthrough to suggestions shot up to 6.54% – an increase of 374%! Direct revenue attributable to this block also rose to 4.8%.

We think that the use of the scroller could have put users off clicking through by displaying more choice and perhaps adds an unneeded level of complexity to the customer’s purchase decision.

However, we think the CTR on the scroller would have increased if, like Amazon, we had displayed “Page x of 3″ on the scroller – so this is obviously worth testing next time!

What do you think? Do scrollers for product recommendations add value or just hinder purchases?

Clever UX/conversion element from @NakedWines. Here’s what you see if you ‘view basket’ when it’s empty. twitter.com/danbarker/stat…

— dan barker (@danbarker) May 25, 2012

I’m not sure if many people without a product already in their basket will click the basket button, but for those that do there’s a great opportunity to recommend a product to encourage a purchase.

I’m also not sure how the suggestion is calculated: it could just be a static product that the team want to sell, or one specially picked for beginners – the name of this product, a “Wine Advisor’s Top 10″, adds the idea of expert curation and social proof.  A dynamic suggestion based on the visitor’s behaviour would most likely provide the best return by understanding the preferences they display whilst browsing, especially for logged in users.

They also encourage visitors to also purchase an Angel membership by highlighting the price for Angel members. By having a dropdown basket only, instead of a seperate basket page, they may be reducing their ability to increase average order value through related cross-sell: our research finds that cross-sells work best on the checkout page – although this may be part of their strategy to reduce barriers to purchase.

So, a great example of product recommendations, and one to test on your site!

So we looked to solve that problem. And the answer was our IntentPredictionServer, a high-speed and flexible recommendation engine. But it wasn’t just any old recommendation engine – it could understand things that other technology couldn’t; content and context: what and why a visitor is looking at, and why they are there. It can adapt any content – banners, product recommendations, text – to ensure that the most relevant is presented to the individual user at the right time, creating a completely personalised experience.

And then the iPhone arrived. Quickly, network operators stopped investing in their downloadable content stores, whilst manufacturer-run app stores blossomed. So we looked at other markets where the issue of too much content was significant.

We arrived in the online retail space. Amazon championed recommendation engine technology in 1999 with their patented “collaborative filtering” technology. “We have 6.2 million customers,” Jeff Bezos claimed. “We should have 6.2 million stores.” But this wasn’t smart technology. Other companies were playing in the online retail recommendation space, but none really pushing the boundaries.

We took a gamble. We pivoted; we left our comfort zone. Were online retailers ready for one-to-one onsite personalisation?

Today, our PersonalMerchant technology is powering millions of unique online shopping experiences – increasing retailer revenues with relevant product recommendations and swapping generic content for more effective, persuasive banners and text.

But at the heart of it, our technology has evolved without forgetting our roots. With zero-latency, carrier grade architecture, we’re computing Big Data in milliseconds to decide the right content to show to each visitor at every step of their journey. Our simple and well documented APIs mean any developer can jump right in and explore the possibilities, hack together prototypes in days, and going from initial brief to live system as quickly as they can.

• Retailers looking to embark upon Big Data products, can – with our technology connecting stock and ERP systems, loyalty systems and CRM data into an automated “single view of the customer”.
• Companies looking to use a highly tuned, automated and personalised decisioning system to provide relevant experiences, can.
• Content providers who want to help visitors discover content, can.
• Businesses who want to take social employment data and match it up to the most relevant job openings in real, can.
• And retailers who simply want to sell more with personalised product recommendations, can.

From today, we’re moving away from just talking about ecommerce personalisation technology. We’ll be embarking on a journey to spread the word about how automated discovery, decisioning, personalisation and recommendation technology can help all businesses.

The digital world has moved on tremendously in recent years, but we’re only at the beginning of a huge movement in the digital world. It’s exciting. So, why not come along for the journey?

Having entered the ecommerce industry around 13 years ago, Zia spoke about mail order catalogue retailers and the issues they had maintaining their core catalogue channel whilst moving online. She said that whilst multichannel seems to be a modern phenomenon, it’s been around for a lot longer than we think.

So we thought we’d take some time to look at what multichannel really means today.

Channels for use

Here’s a short list of the most common channels used by retailers today.

• Online store
• Marketplaces – eBay, Amazon, Tesco
• Mobile
• Catalogue
• Concessions
• Stores

There are only two channels in this list  – marketplaces and concessions – which remove the ability for the retailer to collect and use behavioural and CRM data.

The benefits from joining up data from all other areas of your enterprise not only allow you to gain a “single view of the customer”, but generates behavioural data for use in analysis and prediction. For example, knowing that store shoppers buy smaller size clothes than those online means buyers can better predict and place stock levels; trends can be captured and analysed quicker, and so on.

The issue with multichannel

As well as having a single view of the customer, many retailers are striving to have a single view of stock. With the growth of “click and collect” and services like Shutl (where instore stock is used to deliver purchases within an hour of ordering), being able to pinpoint exact locations of stock will really enable the next generation of multichannel commerce.

However, this is difficult to create and maintain. The ever-changing stock levels of a retail store (plus added issues like theft, returns and exchanges) and varying levels of demand call for a completely integrated, predictive stocklevel syetem.

But with a centralised stock system and thought-through multiple channel connectivity, it’s possible to achieve a “single view of the customer”, plus suggest the most relevant items to individuals at the right time. With the fast-growing adoption of instore iPads, a resurgence of kiosks and focus on improving the instore experience with sales assistant attention the possibilities for not only understanding a customer’s behaviour but sharing it amongst all channels become limitless.

We can help

We’re experts in multichannel personalisation and decisioning – if you’re interested in bringing together your multichannel customer behaviour – contact us.

Around 97% of visits to your site aren’t going to make you money!

Conversion rates in online retail range from less than 1% to possibly around 30% – but the average is stagnant for most, hovering at around 3%

But what do some retailers do? They focus on driving MORE traffic. So that’s even MORE people who aren’t going to buy from you!

This creates a dangerous spiral for your online ecommerce marketer:

more traffic = higher costs
higher costs =  more sales needed
more sales needed = more traffic needed

But by increasing conversion rates, you’re getting a better bang for your buck with your current traffic level. How would you like 1% more orders  without paying to increase your traffic by 1%?

Luckily, we’re beginning to see an increase in a number of different processes and services needed to increase conversion rates.

Conversion Rate Optimisation

CRO is all about testing and optimising your site to deliver a better conversion rate. It includes things like testing buttons and designs, testing product information, layouts,  photos and more – anything which doesn’t need a lot of technical know-how to carry out. For example, Visual Website Optimiser enables you to test and optimise pages without changing any code – complex testing services like Maxymiser are suitable for heavier users.

User Experience/Interface Design

Usually, UX/UI should fall under CRO – but we’ve seperated it out. We believe you by continually testing and improving your user experience (which includes everything from brand messaging, photos and text, etc, used onsite) and your user interface (e.g. checkout process, product discovery and usability), you’ll not only see improved conversions, but happier visitors and customers. Because at the heart of it, retailers sell to people – not percentages.

Personalisation

No, we don’t mean printing your name on a t-shirt. And no, we don’t mean “people who bough this also bought”. We definitely don’t mean personalisation in social care, nor putting “Hi John!” on an email.

We mean really understanding a visitor and what they are looking for. So:

• As soon as a visitor arrives in the “double bed” category on your bed site after searching for “wooden double beds” on Google, show them wooden double beds, not just the five most popular metal ones.
• Upsell similar products on product pages(because these perform 20x better than cross-sells).
• In basket pages, recommend the most relevant products to what the user is buying.
• When they come back to your homepage, show a lifestyle photo of a wooden double bed and recommend matching bedroom furniture.
• Send follow-up emails with special offers on the most relevant mattresses and pillows.

Personalisation has the power to increase conversion rates because it tailors your site for each individual visitor. If I could walk into a supermarket and have a personal shopper who understands your tastes, your requirements and your past behaviour, I’d definitely buy – you’d probably buy more, and I’d probably buy more often.

That’s not to say personalisation is the answer to all of your conversion rate improvement prayers -  you still need to test and optimise all parts of your site to glean as many percentage points as possible. But it does help.

Stop driving more traffic!

Before you increase your PPC budgets, add new traffic sources or waste time on the latest social fad – take some time to think about using personalisation, conversion rate optimisation and UX/UI design to improve sales from your existing traffic.

Case Study

Take a look at our case study with Astley Clarke – nominated for BT Retail Week Technology Awards Internet Technology of the Year, where they realised a 60% overall conversion rate increase with PersonalMerchant personalisation technology, and a 35% improvement for their onsite search conversion rate.

Get the case study now >

Still, it was great to be shortlisted – Neil and Rob from Astley Clarke had a great night at the event, and we’ve got our fingers crossed for being shortlisted for the eCommerce Innovation Award at the eCommerce Awards later this year – we’ll keep you posted!

We’re excited to announce that our partnership with Astley Clarke has been shortlisted for the BT Retail Week Technology Awards Internet Technology Solution of the Year!

Our personalization case study was submitted, outlining the benefits that Astley Clarke have gained from an integrated personalised product recommendations and behavioural search technology – a 60% site conversion rate increase and a 35% search conversion rate increase. The project was installed, tested and deployed within three weeks and is now driving more relevant experiences on Astley Clarke’s UK and US stores.

For more information on how PersonalMerchant can help your business generate revenue through relevance, take a look at our PersonalMerchant information or contact us today.

We believe that initially, there are two important questions to ask when embarking on a personalisation integration project which define the level of data and therefore relevance which can be achieved.

1. What do we know about the content?

For many retailers, this question will be easy. Retailers should already be holding valuable data on each product they stock to:

• add value for SEO
• help purchase decisions
• offer information for researchers, and
• allow better faceted/layered navigation

… so there’s a good starting point to begin with.

There are also other uses for this data outside those listed above – for example, to see trends and provide ideas for blog posts and content marketing!

2. What do we know about how the visitor shops?

This is where it could get trickier. For retailers, being able to understand the reasons behind why a visitor prefers premium brands over own brands or why colour is more important than price.

Through a mixture of user testing or recorded videos from WhatUsersDo, “big data” analysis and consumer research, these traits can be teased out. Not only does knowing these intricate nuances help you better understand your visitor profile, it helps you prioritise product qualities in buying, merchandising and marketing.

Then…

Once you have the answers to the above questions, you can start to create relationships between content and visitor preferences. These could be anything from:

• Those who eventually buy D&G brand products start looking at Versace but are put off by the lack of size information
• There’s a lack of product descriptions for products from the Curtains category
• Visitors who look mostly at red products spend more than those who look at blue.

As well as feeding back this information to departments in your business, these insights can be transferred into a personalisation tool as rules and logic configurations – helping your online merchandising to match your products and visitors, their intentions and the way they shop.

The aim of onsite search is to provide the user with what they have searched for, and it needs to catch misspells and plural variations, understand synonyms, and so on.

The aim of merchandising is to put products in front of users that will a) be relevant to them, and b) encourage them to purchase. This could be done through adapting the order (most expensive first, most relevant, grouped by brand, relevant to the individual with PersonalSearch, etc), promoting certain products above natural listings or through putting relevant banners above results for different search terms).

So, “searchandising” in that context is understandable. But, apparently, it now includes things like navigation, recommendations and personalisation.

Recommendations and personalisation has (almost) nothing to do with search, but everything to do with merchandising. Navigation is a key component of any website – it’s sort of search, soft of merchandising. But at the same time, navigation is different – it’s now seen as a requirement. Searchandising, really, is just a nice to have.

So please, let’s move away from using “searchandising” to describe anything on-site that a journalist or marketer doesn’t understand. Believe it or not, personalisation is personalisation. Search is search, merchandising is merchandising. Yes, they are all intertwined, but it doesn’t mean that they can just all be lumped into one and tarred with the same brush (or each given the same golden halo).

If you’re looking for a definition of personalisation, take a look at our post. But please – let’s stop using “searchandising” to describe anything other than the merchandising of onsite search results.

Here’s the video:

Critique

Within the first two seconds, the user has already declared some important information: he’s shopping for male clothes. Already, with this information, we could begin to skew the site around the user’s gender. This could be simple things, such as adapting banners, or change the product details that are more receptive to men (such as indepth product details - click here for more information on the differences between men and women when shopping online).

The user then clicks onto the Suits category, and tells us that he prefers natural fibres and is shopping for a navy suit. If he had already declared that material interest or colour-preference through previous purchases or browsing, Boden could reorder the category listings to ensure blue wool suits appeared above grey suits made with manmade fibres.

In just 60 seconds, we’ve gained the following information:

• Gender
• Colour preference
• Material preference
• Intent

This data is extremely valuable, and if implemented could be used to power individually personalised recommendations and personalise search results. To find out about how personalised recommendations and search help retailers increase conversions by 60%, take a look at our latest case study.

Once the user clicks on the product page, already there are opportunities to maximise revenue from personalised product recommendations.

Initially, the “Complete the Look” recommended products block is not noticable enough – and this is picked up on by the user. Our best practice for product page suggestion block design are:

• between 4 and 6 product suggestions
• replicate your category navigation layout (usually, this will be horizontal)
• make the block stand out with a different background or coloured border
• think about the title – “you might like” might perform better than “complete the look”

Secondly, instore merchandisers will know that a shopper looking for a suit jacket will probably be looking for either:

• more choice – so show other suit jackets matching the one in view, maybe more expensive (upsells)
• matching trousers
• shirts, ties and other accessories

The question is, how should these be prioritised? We’ve found that upsells perform around 20 times better than cross-sells. In this situation, we’d recommend that Boden offer 6 recommended products with:

• 2 x upsells or similar products from the Suit Jacket category- matching the attributes of the product in view as close as possible
• 2 x cross-sell products from the Suit Trousers category, matching the attributes of the produt in view as close as possible
• 2 x cross-sell products from the Accessories category using “people who bought this also bought” algorithms.

This controlled merchandising rule approach, blended with crowd-behaviour would provide the best of both worlds – increasing relevance for the user and revenue for the retailer.

When the user clicks onto the trousers, shirts are the only recommended products. However, an experienced merchandiser might know that users looking at trousers don’t usually go on to look at suit jackets and that suggesting shirts, ties and shoes will be more effective.

Finally, the user gets to the checkout page. This is a prime location for displaying “no-brainer” product suggestions. These might be:

• under a certain amount (e.g. under £10)
• from low-cost, high-margin categories (e.g. accessories, shoes)
• sale items
• “worst sellers”

All relating to the items already in the basket – using “people who bought this also bought” algorithms or matching brands/colours.

Checkout/basket page recommendations not only increase AOV, but also allow you to promote old stock or “worst-sellers” to run down lines.

Other areas

Category pages drive more revenue through product recommendations than anywhere else. We recommend that category pages are blended in with the natural layout, either by taking over the top line of a category or contained in a seperate block above the natural listings.

The homepage is also a standard place for product recommendations for retailers with a large proportion of loyal visitors. We’d suggest putting personalised product recommendations above the fold and using real-time profiling, falling over to past-behaviour profiling, to suggest the most relevant products to the visitor at that time.

Homepage banners can also be personalised to increase persuasion of a particular category, brand or product and subconsciously enforce that visitors are looking at the right thing for them. For example, banners might change from generic banners to show highly branded related imagery, such as a picture of a skatepark in action for visitors purchasing for a certain brand of trainers.

Let’s not forget emails. Transactional and promotional emails are perfectly positioned to promote the most relevant items for the individual, and it enables completely personalised one-to-one communication.

Score: 4/10

Whilst Boden are on the right tracks with product page recommendations, there are many improvements to be made to maximise revenue. Personalisation technology enables the next level of product recommendations and builds a rich, behavioural profile of each visitor that can also be used elsewhere in their marketing efforts – using personalisation technology effectively could help them unlock higher revenues and higher profits.

What do you think? How else could Boden improve their user experience through better merchandising? Leave a comment below or tweet us @TweetIntent.

Segmentation using demographic classes

But…

You can go further!

Segmentation is great as a theoretical process used to guide strategies and position offerings. But when used as a physical process in a digital medium, such as email marketing or online advertising, it becomes complex and hard to measure. Suddenly, your theoretical AB segment that makes up 20% of your 6m customer base becomes a very real group of 1.25million influential and wealthy individual people.

Segments of 1

How would you maximise revenue from that group of AB customers? You could look to segment deeper, using perhaps psychographics or other qualitative metrics. Or, you could begin to treat each individual customer as just that – an individual.

A poor graphical representation segments of one

In the representation above, a segment of one is understanding, and therefore being able to target, one individual.

In this post, we’ll look at 5 steps you need to take to move beyond segmentation and start creating dynamic and relevant experiences that truely understand the individual.

1: Audit your digital touchpoints

Your customers and visitors flow in and out of purchase journeys and interact with many different touchpoints, so map these out. Include your social networks, email marketing, direct mailings, brand and non-brand inbound SEO and PPC, mobile sites, call centres and in store activity.

The data that can be collected from each touchpoint is extremely valuable. If a customer isn’t engaging with your brand through direct mailings but is really active with your social spaces, then it’s worth reevaluating the effectiveness of reaching them through DM. Every interaction can be related to an individual – in the US, Target attribute every transaction and behaviour from purchases online, instore or competition entries to a unique Guest ID.

Using the behaviour that customers generate combined with your product set, each digital touchpoint can be dynamically adapted using past and real-time behaviour. The task is just in working out how this is done effectively with the best results for both revenue and experience.

2: Get your product data in order

You can’t expect to understand your customers and visitors until your product data is clean and normalised, with key attributes such as “colour” and other corresponding data standardised across categories. Not only will this provide a clean base for your personalisation and “segment of one” segmentation efforts, it also helps by:

• allowing more standardised faceted navigation  in your store
• provides more data for your onsite search facility
• improving SEO if attributes are brought out on product pages

3: Define goals – what do you want to do?

“Make the user experience more relevant”; “Increase revenue by cross-selling”; “Create dynamic environments optimised for persuasion”.

Whilst the above are great for defining broad strategies, they don’t say exactly what you want out of “segment of one” targeting.

Each touchpoint needs to have clear defined goals for each possible visitor context. You also need to make sure you’re going to consciously ahieve business goals – for example, if you want to increase revenues then show more expensive products over others; if you want to increase profit then show products with a certain profit margin or higher.

4: Implement the website targeting piece

Find the right technology for you. For example, if you have many digital touchpoints then a provider that is able to collate data from different sources will be needed; if you just have a website, another might  just be enough. There are many other considerations – for some advice take a look at our technology guide or get in touch with us today.

5: Optimise and extend

After implementing, we suggest you watch the results for the first month and allow for behaviours to be mined and relationships to be formed. After this initial period, you can then start optimising your “segment of one” strategy. Most providers will allow you to split test different algorithms and it’s in this period that you can play around before settling with a specific strategy that works for you. Rather than continuously test and optimise, we suggest to revist tests for the first three quarters of the year – setting your “best” strategy on 100% for the Christmas period!

Once you’ve got your site humming and producing results, you can then begin to extend your strategy elewhere. For most retailers, we suggest looking into sending personalised, relevant to the individual emails – both transactional and promotional. Relevant product suggestions and content in segmented email campaigns increase clickthrough rates and revenue generation.

Other areas to explore extending personalisation and segment of one technology into are:

• mobile/tablet sites
• social networks – through social login or suggestions on social networks
• instore kiosks
• POS systems and receipt promotions
• Digital signage
• Direct mail

Let’s talk!

The above blog post has hopefully introduced you to 5 key steps to help you move beyond mass-segmentation and into more relevant segment of one dynamic marketing. Personalisation technology helps you understand the individual over multiple touchpoints, providing more relevant experiences to every customer – not just those who fall into a pre-defined bucket!

If you’d like some more information about how we can help with your segment of one strategy, get in touch with us today!

Here are some of our top tweets from the hashtag stream:

Well said! We don’t look at e-this, m-that, we just look at commerce. They’re all customer touchpoints. Dan Lumb from #reiss #drapersecomm

— wgsn (@wgsn) February 28, 2012

Pinterest is third biggest driver of traffic to #my-wardrobe after paid and organic search. #drapersecomm

— wgsn (@wgsn) February 28, 2012

Topman’s Gracia Amico says “if you like this, you’ll also like these” strategy is great online conversion driver #drapersecomm

— Ana Santi (@Ana_C_Santi) February 28, 2012

RT @Ana_C_Santi: Personalisation is at the heart of ecommerce success says Shop Direct’s James Balmain #drapersecomm

— PredictiveIntent (@TweetIntent) February 28, 2012

On online merchandising. Always think about the fold. Shop Direct tests show leap in conversion when product displayed higher #drapersecomm

— wgsn (@wgsn) February 28, 2012

• notify visitors of the cookies they use and what each cookie does
• ask for explicit consent from the visitor on the first page load.

The EU Cookie Directive, which has come to be known as the Cookie Law, will present a massive step backwards for site operators who have grown accustomed to the masses of data available for analysis and more. But, there still aren’t any clear examples which do the job in a user-friendly way.

The Law

a person shall not store or gain access to information stored, in the terminal equipment of a subscriber or user unless the requirements of paragraph (2) are met.

(2) The requirements are that the subscriber or user of that terminal equipment
(a) is provided with clear and comprehensive information about the purposes of the storage of, or access to, that information; and
(b) has given his or her consent.

Regulation 6 of the Privacy and Electronic Communications (PDF)

What The Law Means

Firstly, visitors must be clearly notified of the cookies that will be stored and be given comprehensive information about what each cookie does. Secondly, they must give their explicit consent for the cookies to be stored.

Let’s remember, that this is for any cookie set on a website. For example, most sites will set a session cookie at the very least; more complex sites will set third-party advertising cookies, preference cookies, past-behaviour cookies and more.

The comprehensive information you need to provide can’t just be “We set cookies to improve your experience”. The information should explain:

• what a cookie is
• why they are used on your site
• what cookies, or the categories of cookies, are set
• an example of what they do

Is There Anything Else?

Yes. There are several use cases for exceptions.

Some cookies are “strictly necessary” for the “provision of… services… requested by the… user”. An example given by the ICO is that of an online retailer, where a cookie is “used to ensure that when a user… has chosen the goods they wish to buy and clicks the ‘add to basket’ or ‘proceed to checkout’ button, the site ‘remembers’ what they chose on a previous page.”

This also includes load-balancing cookies and cookies set for security (by an online banking service, for example). However, cookies set for analytics or advertising are not seen as strictly necessary, and so need to be given explicit consent in order to be dropped.

What About Browser Settings?

Browser settings aren’t enough – yet.

…if the user visits a website, the website can identify that their browser is set up to allow cookies of types A, B and C but not of type D and as a result can be confident that in setting A, B and C they have the users consent to do so. They would not set cookie D.

At present, most browser settings are not sophisticated enough…

But it could become an option in the future.

Anything Else?

Yes.

The ICO guidelines make allowances for instances where a visitor is given clear notification and the chance to explicitly consent, but then clicks on an internal link elsewhere on the page.

In this case, all cookies can be set on what is effectively the second page load – as long as the initial notice is clear, you can infer that they have “actively indicated they are comfortable with cookies”.

The ICO does say though that you may want to prominently display a notice to remind users that you have set cookies.

Will We Be Prosecuted For Dropping Analytics Cookies?

Probably not.

Whilst he does not consider they are exempt from the rules the Commissioner is therefore unlikely to prioritise, for example, first party cookies used for analytical purposes and cookies that support the accessibility of sites and services…

How To Get Started

There are many different interpretations of the law, but below we’ve identified three steps to ensure you’re ready for the Cookie Law.

The First Step For Site Operators

Site operators need to firstly carry out a cookie audit. This means looking at:

All of the cookies your site sets and why

This is useful not just for this purpose, but because it can also help reduce things like page loads and get rid of redundant cookies which you may still be setting.

How intrusive each cookie is

The ICO document notes that “although the law makes no distinction between different types of cookie it is intended to add to the level of protection afforded to the privacy of internet users.”

Effectively, this means that the more intrusive your cookies, the more you should think about changing how it is used – although there is no need to notify users of how intrusive the cookies you set are.

Whether a cookie is “strictly necessary”

In some use cases, there will be cookies that are strictly necessary and that abide by the “spirit of the law” set out in the regulations – in which case these can be set automatically, without the need to gain consent. Remember that only cookies which are strictly necessary for the provision of a service requested by the user can be set.

Everything OK? Now Find a Solution.

This really will be the Holy Grail of ensuring you keep as any users clicking “I agree” from 20th May onwards.

Below, we’ve listed and critiqued a few cookie law solutions – but we’re sure there must be more we haven’t found. Feel free to email your suggestions to james@predictiveintent.com and we’ll add it.

Cookie Collective

The Cookie Collective’s solution is very nice: it’s polished and is based on getting visitors to click Allow – visitors can’t escape from the bar or close it unless they Accept.

However it only allows visitors to accept all cookies and may not be the right option for site operators looking to allow visitors to choose the type of cookies they want to allow.

The solution starts at £295 +VAT per year, but is fully managed – their team will audit your cookies, write copy that complies with the “comprehensive information” requirement.

Score: 8/10

Portent

Having only recently come across Portent, it’s a nice and simple (free) solution – however it’s not certain that it complies with the regulation.

On the first page load, a standard dialog box will appear asking the visitor to accept cookies. It doesn’t provide any “comprehensive information” on the cookies used. If a visitor ‘cancels’ the box, they will be redirected to a seperate privacy policy page (which can’t set any cookies).

We’renot confident that this solution could meet the requirements for the law, and I don’t think it provides a good user interface. However, we do like the fact that it will check to see if the visitor is from the EU before showing the box – and so, could be a contender for internationally-based businesses with UK traffic.

Score: 2/10

Jpecr from Wolf Software

The free Jpecr package from Wolf Software has a lot of good points. Visitors can be presented with the notification in a number of different ways (a top/bottom bar, a hover box, automatically appear from the top or appear as a modal window); site operators can input clear and comprehensive information about both why they are using cookies, what they do and go into detail on the exact cookies used. We like  that users can select which cookies they want to recieve – therefore being able to opt-in to Facebook cookies but opt-out of 3rd party advertisers.

But, in binary opposition to the previous solution, this gives the user too many options. Take a look at their (very comprehensive) demo page, and try Demo 8. From a consumer point of view, you would be scared if you arrived on a site and were presented with that.

This could very quickly become a winner if they enhanced it with some aspects from the Cookie Collective solution.

Score: 6/10

CookieQ

The Cookie Q Solution seems to be a bit more complicated than any others, as they focus around the “button”, rather than the clear notification – although an optional banner is provided. The banner doesn’t offer the “comprehensive information” on the cookies that will be set and what they do, but they do say that they have a “gated” Facebook Like system and that, interestingly, you can still collect useful statistical information about 100% of the visitors whether they have opted-in to cookies or not.

They do focus on “buttons”, so for site operators looking to implement their own notification bar/modal window solution but looking for the technology to control the opt-in, this might be one for you.

Score: 6/10

Cookie Control

Again, it’s not certain that this complies. Firstly, the notification options aren’t clear enough – the notification shows on each page load, then disappears after a few seconds into the orange triangle (a diamond shape is also available). The notification doesn’t show “comprehensive information” and only links to a site’s privacy policy. However, the authors say that more advanced users can go further with the product.

Score: 5/10

Any others?

Email me at james@predictiveintent.com and we’ll write up a review.

Closing Thoughts

We’re surprised that there’s no single solution which uses the “second page load” exception, or any that are really designed for the user – rather, they seem to be designed in a complicated way by developers with no idea about user interfaces or persuasive design. Something for an innovative entrepreneur, perhaps?

Your Opinion

What do you think will happen on May 20th? Will consumers all over Europe wake up to ugly modal boxes, or will the ICO hit corporations with enforcement notices?

I think my biggest issue with those articles is that they don’t really know what personalisation is –  there’s not a single, standard, solid definition of personalization. I think the internet community need this to move on effectively. Wikipedia states that personalization “involves using technology to accommodate the differences between individuals.” But I think that’s too broad.

So I’d like to put forward a definition of my own.

Defining Personalization

Personalization technology enables the dynamic insertion, customization or suggestion of content in any format that is relevant to the individual user, based on the user’s implicit behaviour and preferences, and explicitly given details.

In a nutshell, I think personalization helps any format present or suggest content that is relevant to the individual at that moment in time, based on implicitly and explicitly given data – dynamically.

Personalization Isn’t Recommendations

In 1999, Jeff Bezos and his Amazon employees were sitting in Seattle, thinking hard about how to make more money. And they started to look at collaborative filtering.

“…collaborative filtering… [is] able to help people to discover exactly what they’re looking for, saves them time and improves their lives… We have 6.2 million customers, we should have 6.2 million stores.”

Collaborative filtering is the technology behind simple recommendations which looks at the relationships between users (customers) and items (products). Whilst effective in their own way, “People who bought Product X also bought” and “People like you also looked at” are supremely simple algorithms.

And there are lots of companies peddling these simple product recommendations as “personalisation”.

As Hank says,

“These recommendation engines were once ground-breaking, but they have failed to evolve. And more importantly, our expectations as Web consumers have evolved beyond the simple concepts of “users who purchased item X also purchased item Y.” At best, services that claim personalization based upon these aggregate metrics attempt to triangulate an identity for us as individuals based upon the galaxy of other individuals. They try to pin us down into an archetype, into a box of likes and interests, without recognizing that as humans, what we desire, want and need is in constant flux and ever-evolving.“

Recommendations look primarily at the crowd. But personalization, as the name suggests, needs to look at the individual person.

As he says, there are providers out there who claim to offer “personalization” services, but looking at their websites and implementations, it’s clear to see that they’re faking it. One in particular seems to sell crowd-based recommendations dressed up as “personalization”. And that’s just not right.

What Is Personalization?

Let’s dissect my definition.

“Personalization technology enables the dynamic insertion, customization or suggestion of content” – personalization doesn’t just have to be product recommendations: it can also include inserting any content like images or text (e.g. displaying a golf-orientated banner for a returning golf supplies buyer), or customizing content that is already there (e.g. “Hi Joe, we’ve got some great movie suggestions for you!”).

“…in any format” – it isn’t restricted to the web. It can be implemented for any medium or touchpoint, such as emails, apps, instore kiosks, etc.

“…that is relevant to the individual user, based on the user’s implicit behaviour and preferences, and explicitly given details” – finally, the most important part. Personalization uses both implicit and explicit information, derived in two ways. Firstly, a visitor might explicitly declare some information, such as their gender or date of birth.

Secondly, their behaviour can be mined and processed to help understand affinities and relationships. A good example of this in action would be on a clothes store. If you haven’t given your gender, but in the last 4 clicks you’ve only looked at men’s clothes, it’s pretty safe to say you’re shopping for some men’s clothes – so don’t recommend women’s clothes for this session. If you’re a regular returning visitor and you have bought 4 blue items and one purple item, you can be profiled as having an affinity to the colour blue – but let’s not forget that you like some purple in your wardrobe too.

And it doesn’t have to stop at simple product attributes like colour, size, gender or similar. At Netflix, they have a team of specialists classifying each film with plot details such as “Strong Female Lead” to add more data for real-time and past behaviour profiling. By adding their expert knowledge into the system, Netflix’s personalized recommendation engine becomes smarter as it understands the subtle nuances that users can subconsciously relate to.

And, this leads nicely onto Part 2 – Curation isn’t Personalization.

It will be available to read on Friday 17th February – to get access now,  add us to your Google Plus circles and get the password!

Part 2 – Curation Isn’t Personalization.

I’ve read Nir Eyal’s article about ten times, trying to understand what he was saying. And I totally disagree.

He declares:

“Pinterest will be the first company to nail eCommerce personalization…Pinterest is becoming the web’s personalized mail-order catalog. Each user is presented with a one-of-a-kind visual interface based on their tastes.”

No, it doesn’t. Well, it sort-of does, but in a very roundabout way. Let me explain.

First of all, Pinterest isn’t ecommerce – you can pin anything, not just products, and I doubt that their interface will ever become mainstream for anything else than Pinterest-like sites. It’s just another marketing channel, like Twitter.

Secondly, it’s not personalized.

Nir writes:  “… items [which Pinterest users] see are curated through people and topics they’ve identified as interesting and what is shown to them improves the more they interact with it. Every time they pin, re-pin, like, or comment on an object, the relevancy of the products displayed on their magic catalog improves.” I don’t think they do.

I asked Pinterest expert Vikki Chowney what she thought of Nir’s comments. “I’ve not heard about this functionality within Pinterest, so either Nir has some inside information, or he’s misunderstood. If Pinterest did adapt based on relevancy, in my opinion you’d miss out of much of the ‘discovery’ within the community you build – which is based on ‘Pinners you follow’. That after all is part of the appeal.”

At any time, someone you follow could pin something so totally irrelevant to you. So let’s agree that no – Pinterest don’t personalize. In one sense, yes – they allow users to say “I want to follow this person or this topic because I’m interested in what they are sharing” – a form of social discovery, customise their feed. But, that’s not personalization. It lets you follow curated feeds and nothing more.

Imagine that Netflix was more like Pinterest. You’re really interested in romantic films, so you might follow a romance specialist. One day they say you should watch The Notebook, the next day they suggest Jack and Jill, starring Adam Sandler. They’re totally different films genres, but loosely can be classified as “romance”. But Jack and Jill is totally irrelevant to you, because you hate comedy films.

However, it is important to allow users to declare new interests, to ensure they don’t get stuck in what we call the Locked Loop, a continual and relentless regime of repetitiveness where users can’t escape from the Filter Bubble. But this can be done through menu navigation – it doesn’t require a long-winded social discovery process.

So now we’ve got that cleared up.

Part 3: The Future of Ecommerce Personalization

I want to borrow some words from Hank’s article on Techcrunch again.

“Groupon knows that targeting by regions increases conversion and sales, but imagine how much they could amplify that effect if they were targeting based on a rich and sophisticated understanding of the individual person that receives each offer?”

Online retailers should be itching to move beyond recommendations into personalization. A better understanding of each individual visitor and customer can help retailers to provide better experiences; to suggest more relevant products and to maximise the chances of a conversion.

Here are just a few examples of where personalization can make a difference and change the face of ecommerce.

Product Recommendations

Initially, driving product recommendation areas with personalization logic will lead to much more relevant product suggestions. Retailers can use business rules and their own merchandising expertise to curate personalized suggestions – for example: in jewellery, a retailer may know that buyers are more susceptible to upsells; in electronics, BestBuy might know that potential customers usually look at products around their maximum pricepoint, so don’t show more expensive products – instead, suggest similar products that have a higher margin.

I think that crowd-based recommendations have their place in a personalization strategy. Cross-selling is a big part of helping to improve the average order value metric by suggesting relevant products – but there always needs to be an element of personalisation. I think the best way to achieve personalization is by filtering the crowd. For example, if I’ve added some new sneakers to my shopping cart, don’t show me Nike products because you know I’m a slave to the Adidas brand – so show me the most popular Adidas products bought by people like me.

Search

One of my biggest bugbears is searching on a clothes store for a generic term, like “t-shirts”. You know I’m a man – please, only show me men’s clothes.

Any online retailer can now use individual visitor behaviour to provide personalized search results that are optimized to encourage conversions. The clothes store is a great example – there are so many different data-points available which can be used for real-time and past behaviour profiling such as gender, size, preferred colours, particular affinities to brands or designers and so on.

Imagine an online foodstore, where a visitor has previously and explicitly declared they are lactose intolerant. When they then use the onsite search feature, there’s more chance of a conversion if dairy-free foods are surfaced to the top. It’s the simple things like this that create smoother experiences and will improve other metrics such as loyalty.

Emails

Like Leena picked up on in her article, personalized email marketing is growing because it’s infinitely more relevant than segmented email marketing.

“Recently, I started to receive emails from Gilt Groupe that suggested similar earring to like those those I had added to my wait-list on the e-commerce site. The company also sends personalized email notifications on sales that are tailored to each customer… And brick and mortar retailers like Saks Fifth Avenue, and many others are also starting to jump on the personalized email bandwagon.”

With personalization, retailers don’t need to spray n’ pray – segment-of-one emails put the right content and product suggestions in front of the right people.

Social

I’m a bit of a social commerce sceptic – but I do see the value in obtaining and using social data in personalization. Knowing a visitor Likes “Levi’s” on Facebook is an instant declaration of interest. But to be done right, it should be part of a more all-encompassing strategy, using the most relevant data in the most relevance places.

What’s Next In Personalization?

Its ok, you can relax. I think we’re only at the beginning of what will be the biggest disruption in the web since social media.

Recommendation engines paved the way; Amazon led the charge. But now it’s up to each and every online retailer to step up to the plate and have the same lofty, but much more obtainable, ambitions as Amazon did in 1999.

“There should be the optimum store for each and every customer.”

But, please, please remember that personalization isn’t crowd-recommendations, or curated content! Let’s learn to crawl, before we start walking and running.

So here’s our definition for personalization:

Personalization technology enables the dynamic insertion, customization or suggestion of content in any format that is relevant to the individual user, based on the user’s implicit behavior and preferences, and explicitly given details.

What do you think? We’ve got a longer explanation coming soon, but just wanted to get your thoughts. Tweet us @TweetIntent with your thoughts.

The partnership enables online retailers using the Cshop platform to quickly begin increasing sales and conversions by up to 35% by optimising their sites into dynamic selling environments, based on their visitor’s behaviour.

As well as powering Amazon-style  product recommendations, PredictiveIntent’s Suggestion Choreography technology understands a visitor’s unique context and allows retailers to utilise over 160 smart algorithms to target visitors with product recommendations, personalised banners and other content at each stage of the purchase journey over multiple digital touchpoint including promotional emails, mobile sites and social networks.

“We found the integration to be straight forward and can see the benefits this will bring our clients on the Cshop platform”
Garry Billson, Technical Director, Cshop

“Ecommerce personalisation is fast becoming a must-have technology for retailers of all sizes. We’re pleased to be partnering with Cshop to allow their online retail client base easier access to the latest behavioural technology to drive increased revenue and happier customer journeys.”
Neil Hamilton, CEO & Co-Founder, PredictiveIntent

Cshop retailers wanting to find out more about increasing conversions with PersonalMerchant can contact their account manager, or visit http://www.predictiveintent.com for more information.

ENDS

About PredictiveIntent

PredictiveIntent’s advanced behavioural personalisation technology allows retailers to have full and transparent control over their onsite personalisation strategy, helping retailers to grow revenues and conversions by placing the right products in front of the right people at the right time.

For more information, contact James Doman, marketing manager, at james.doman@predictiveintent.com or +44 (0) 1202 832030.

About Cshop

Cshop is a comprehensive Ecommerce platform to fit all multi-channel businesses. Packed with features to help increase sales and deliver services, Cshop is backed 24X7 by a helpful and knowledgeable development team.

Cshop has been the choice of some of the UK’s most successful retail on line businesses for almost 12 years, and our team are ready to assist with all your on line needs.

Personalisation can’t fully exist without recommendations.

We see personalisation as using visitor behaviours, preferences and other information to suggest content – that content might be products, banners, or text. The type of behaviours we use range from buys and browses to remove-from-cart’s – they’re all valuable nuggets of information that help our algorithms calculate relevant content.

Understanding visitor preferences and other information is the first step to personalisation. For example, if a visitor looks at more red products than blue products they might be more likely to buy a red product. Knowing a visitor’s size means you can best merchandise your site around products to suit them. Even knowing their gender from a sign-up form is enough to begin adapting the experience.

Personalisation as a filter

Our framework consists of over 160 algorithms that look at the site, visitor, content, session, search terms used, or attributes.

By running these algorithms, and then filtering by our understanding of the visitor’s preferences and intent, we can best match content with visitors.

For example, a store might have the Widget t-shirt, with both blue and red versions available as child products. It might turn out that the blue version is the best selling item on the store. A visitor with a preference for red arrives on store. Standard personalisation might just ignore the red version of the best selling blue Widget top, and suggest other blue products similar to what the visitor has bought/looked at before, the visitor can find themselves in a Locked Loop.

By running our understanding over other algorithms (rather than just finding similar products), and with our Replacements technology, we see the best selling blue Widget t-shirt, know there is a red version, and will suggest it – presenting a much more relevant product suggestion than a old-season red t-shirt.

Just as you find a diamond in the rough, the jewel in the crown; personalisation is best served by the filtering of the crowd.

No Search Results Found.

Especially when I know the site does sell what I’m looking for. Ok, I might spell something wrong, or use the wrong word for something, but asking visitors to try again can disrupt their journey, and even annoy them.

Why does it appear, and how can it be avoided?

There are three main reasons why a specific search might not surface any results.

Fuzzyness

You like potato and I like potahto,
You like tomato and I like tomahto,
Potato, potahto, tomato, tomahto!
Let’s call the whole thing off!
Ella Fitzgerald and Louis Armstrong

Mispells, typo mistakes, plural variations and sound-a-like phonetics all present extra challenges for ecommerce search engines. The very nature of online shopping (offering a wide range of products to a wide (possibly global) audience) means spelling mistakes are inevitable.

Rather than educate each one to spell properly, try using “fuzzy” search algorithms – these are readily available through various ecommerce platform plugins, APIs or third party outsourced services (including our PersonalSearch ecommerce search).

Fuzzyness uses algorithms to look at the characters used and swaps them until it can find results. For example a search for “Calvin Cline” would find “Calvin Klein” results by removing, swapping and adding characters.

Synonyms

I use a laptop, but you might call it a notebook. Users searching for “ipads” might just mean “tablet computer”. Synonyms rear their ugly heads in in all industries, from clothing to electronics, beauty to office equipment.

Synonyms are different words that have the same meaning – if you’re not catering for these in your ecommerce search then you’re probably missing out.

They’re easy enough to implement – most ecommerce platforms can handle these, they can be managed through third-party search tools or they can be set as a specific attribute.

You just don’t sell it!

Not everyone is Amazon. Sadly, you can’t offer every product on the planet, as much as you’d like to.

How can it be an opportunity?

“No results” pages are great for personalisation, using one important algorithm to find relevant products.

Similar searched and bought

This type of algorithm looks at the search term used by the visitor, and looks to see if any other visitors used the same term, then looks at the products they eventually bought.

Here’s an example.

• A visitor searches for “notebook” on an electronics store, and the store doesn’t have any synonyms set up. The search returns no results.
• Other people have also searched for “notebook”, have seen no results and then used category navigation to find and eventually purchase a laptop.
• In this case, the most popular laptops bought by others who searched for the same term would be displayed on the “no results” page.

To maximise the chance of predicting the most effective product for the visitor, personalisation filters can ensure the returned products are relevant to the individual. Using past behaviour, we can mine visitor preferences and show, for example, a visitor who has shown interest in “Sony” products, a Sony laptop as part of their “no search results” page.

Consciously test and optimise to achieve your goals

If you could, you would want to sell each customer a more expensive version of the product they are buying. Business rule support lets you achieve higher revenues, increase profit or shift old stock by skewing suggestions so that, whilst remaining relevant to the visitor, they have your best interests at heart.

Summary

• Use a search tool that uses “fuzzyness” and supports product synonyms
• If you can’t find any results, show “people who searched this eventually bought” product suggestions

If you’d like to find out more about how personalisation and search technology can work together, get in touch today! Contact us, LiveChat with the blue box in the bottom right-hand corner or tweet us @TweetIntent.

Here’s the graphic:

The graph shows the percentage of revenue driven by different areas that are dynamically merchandised by our PersonalMerchant ecommerce recommendation engine technology.

PersonalMerchant helps retailers optimise their store into dynamic selling environment. For example, PersonalMerchant can adapt product recommendations to be as relevant as possible for each individual visitor. Not only does this improve visitor journeys by reducing the need to browse and search, it helps retailers increase revenue and conversions by putting the right products in front of the right visitor at the right time.

As you can see, category pages drive the most amount of revenue – an average of 24.2%. Retailers using this space are consciously achieving business results by skewing product recommendations by rules – for example, show “worst-sellers” to shift old stock, or show “most expensive” suggestions to increase revenue.

The second best-performing location is the homepage. Dynamically driven PersonalMerchant blocks on the homepage can use profiled information to present returning visitors (who are most likely to arrive direct to the homepage) with the most relevant products based on their history. Our unique, patent-pending Suggestion Choreography technology allows different algorithms and “logic” to adapt based on the indivdual visitor’s unique context – for example, once a returning visitor begins their journey our smart algorithms will change from using “profiled” behaviour to “real-time” behaviour, allowing for personalisation based on intent.

Retailers wanting to cross-sell products, in order to increase average order values and average basket sizes, can see an average of 4.6% of revenue driven by checkout page suggestion blocks. These areas, also driven by PersonalMerchant, allow retailers to promote other products, such as accessories, using a mixture of “people who bought this also bought” algorithms (filtered by business rules) and predictive, profile-based suggestions.It’s important to test checkout page suggestions, as they can have adverse affects on conversion rates.

Product page suggestions, as covered in our last blog post, are best used to up-sell similar but more expensive products. Up-sells should be smarter than  just “same category, but more expensive” suggestions – taking into account a visitors profile and preferences can make that all important step from a £30 product to a £50 product – increasing your revenues and profit.

Worst-performing, out of the locations used for this research, was Order Confirmation pages. Typically, these are pages presented to visitors after they have checked out and completed an order. A simple reason for underperformance may be that customers have completed their task, and require nothing else. Testing this location with different types of algorithms will increase the performance, especially if discounted or impulse “no-brainer” products are suggested.

What do you think?

How do you think product suggestions in different locations can help a retailer, and help a visitor along their journey? Tweet us @TweetIntent and we’ll post the best responses here.

This statistic stood out.

Up-sells perform around 20 times better than cross-sells on a product page.

An up-sell is where suggested products are similar to the product in view, but more expensive – a great way for retailers to increase their revenue.

With our PersonalMerchant ecommerce personalisation technology, our up-sells are decided not just through price and category based rules, but by using  individual visitor behavioural data to ensure we are up-selling the most relevant products. And it looks like it’s working!

It demonstrates that product page suggestions are not that effective when using the simple “people who bought this also bought” algorithms commonly used for cross-sell suggestions.

By better understanding the visitor and their unique preferences, behavioural up-sell product suggestions leads to more clicks on upsells (10 times more clicks than cross-sells), and, most importantly, result in up to 20 times more sales and revenue.

For retailers now thinking ‘how can I effectively suggest accessories if I can’t cross-sell’, think again.

How can I cross-sell more effectively?

Checkout page cross-sell suggestions receive, on average, the second highest amount of clicks after homepage product suggestions. Using “people who bought this also bought” algorithms, combined with maximum price rules and filtered by personal preferences, retailers can realise an average of 3% of revenue from effectively placed product cross-sell opportunities on the checkout page.

We’ll be releasing more stats and numbers over the next few weeks – make sure you’re signed up to receive our GoodIntent newsletter for a monthly roundup of personalisation and ecommerce industry news and views! Sign up with your email in the footer below.

What do you think?

The above is the aggregate data from all of our PersonalMerchant clients – have you experienced something different? Are cross-sells more important than up-selling?

Tweet us @TweetIntent with your views and we’ll add them to the post!