Could big data help retailers compete with Amazon?

Credit: Thierry Gregorius via Flickr

On its 15th birthday, Amazon has announced its best-selling product list. For a company that started out as an online bookstore, it’s no surprise to see The Girl With The Dragon Tattoo and Fifty Shades of Grey on that list. Call of Duty reflects the company’s evolution into the online entertainment retailer, while HDMI leads and Memory cards reflect a company that has become the ultimate retail platform in a connected age where many customers are mobile-first and showrooming.

They’re the gold standard of online retail. Their mission “to be Earth’s most customer-centric company, where customers can find and discover anything they might want to buy online, and endeavors to offer its customers the lowest possible prices” is something I’m sure many board rooms have aspired to in the past, but Amazon have certainly delivered.

The impact this has had on the high street has been well-documented. Our Price, Comet, Virgin Megastore, Zavvi, Jessops, Woolworths went belly-up citing online competition as a major factor. But it’s not just about their first-class retail platform and super-low prices. They are up there with Apple, Facebook and Google when it comes to big data, customer insights and experience. Understanding the customer, and effective CRM to meet their needs, are critical for any retailer to prosper – and with its data set Amazon has become king of the hill.

As a relatively young company Amazon did not have to battle with a traditional way of doing business or adapt to the new world. As online-natives they have always had the edge when it comes to data, and have created a daunting prospect for any CMO asked to create a customer experience “like Amazon’s” without the vast amounts of customer data required to deliver. Where chickens are free range, organic, fair trade or basted and toilet paper comes with multiple pleats, aloe vera or a donation to charity – consumers define themselves through their purchases and it’s the brands that understand what is truly important will succeed – data helps CMOs to understand what motivates.

So we think predictive analytics will become increasingly important for CMOs – enabling them to plug gaps in their own data, and deliver these opportunities  by working with companies that spend their time in understanding consumers. We have found that using predictive analytics can not only increase conversions three-fold, by identifying ‘look a like’ customers from an ‘unknown’ population – but can be used to use to personalize and optimise their site experience for loyalty.

Since it is now virtually impossible for any brand to compete on the functional – product features, delivery speed and costs – it is more important than ever for CMOs to use the power of their brand. By understanding the motivations, aspirations, attitudes and values of their customers there is a real opportunity to create relevant experiences that drive loyalty.

Consumers will not be best-served by a Model T Ford retail landscape where “you can buy from any retailer you like, as long as it’s Amazon”: so big data, and the insights that come with it, will be increasingly important.

 

 

How publishers can combine predictive analytics with CRM to convert registered users into paying subscribers

I know I’m wasting half my marketing budget, I just don’t know which half

It’s a well-worn marketing phrase and reflects a problem many would like to solve.  The better you understand your customers, the more effective your marketing – so we’ve been looking at how to apply the profiling technology we use in advertising to CRM, to see if we could predict which ‘unknown’ customers are most likely to buy.

VisualDNA has invested heavily in personality profiling technology. Using patented visual-based personality quizzes, we are able to profile individuals online with great detail and accuracy, and subsequently use behavioural models to extrapolate this detailed knowledge onto hundreds of millions of users. We group people by various characteristics into ‘segments’ – anonymised groups that can be used by businesses in communications, usually targeted advertising, product recommendation or content personalisation.

There are other applications too, one such area being CRM. Most businesses, whether they are business-to-business, or business-to-consumer, possess a database of customers, or active leads. And those customers can be categorised in all sorts of ways. But whichever way you define your customer base, it’s notoriously hard to predict who is most likely to buy.

So we wanted to test the idea that the inference algorithm we use to build our high-performing segments could also be used to identify which customers are most likely to buy out of any given data set.

Piano Media is a company who run a subscription paywall business on behalf of a large number of publishers in multiple countries in Europe; with a massive data set of customers who fit one of three categories;

1) Unknown / un-registered,

2) Registered but not subscribed,

3) Paying subscribers.

Clearly the objective for any business operating a freemium model is, having attracted the customer in the first place, migrating your unknown and registered users into paying customers. The question is, how do you know which of your registered users are most likely to become paying subscribers? At which part of your audience do you direct your marketing?

Working with Piano, we drew a random sample of 30,000 registered non-subscribers and compared their online behaviour to that of the entire population of paying subscribers; using what we call Look-alike modelling. We did this completely anonymously, using randomly generated anonymous identifiers stored in cookies.

From here we were able to assign a similarity score to every one of the 30,000 registered users – the score representing how similar user’s behaviour is to the paying subscribers and therefore how likely we think this person is to convert to a paying subscriber.

Piano then did three mailshots, under exactly the same conditions, to three separate groups within that population

• The top 5,000 (those with highest score)

• A random sample of 5,000 users

• The bottom 5,000 (those with lowest score)

The results are hugely encouraging. On the top 5,000 Piano got a conversion rate of 1.59%, compared to 0.52% for the random group and 0.22% for the bottom group. In other words, our algorithm correctly predicted those most likely to convert, based on the theory that the online behaviour of a paid-up subscriber was sufficient to build a profile on them and use that as a means to find those with a similar profile. Moreover, we correctly predicted that users with behaviour dissimilar to paying customers are a lot less likely to convert than random users, so that marketers don’t need to spend the money and energy marketing to these.

This result has significant implications. It means we can, in theory, take any database, whether online or offline, anonymously profile anyone in that database, and use our inference and Look-alike algorithms to predict behaviour to improve the effectiveness of a company’s marketing – in this case increasing conversion more than three-fold vs a random sample and weeding out those least-likely to buy.

Find out more.

How publishers get a real time understanding of their audience through WHYanalytics

This interview first appeared in US digital marketing blog The Makegood under the title “VisualDNA’s Ian Woolley on Real Time Understanding”

Ian Woolley is Chief Commercial Officer at VisualDNA, an audience insights company with patented technology helping businesses understand people online. The company is headquartered in Shoreditch, London and Ian joined the team just a few months ago. The Makegood recently spoke with Ian about VisualDNA’s products and vision.

The Makegood: Congratulations on the launch of Real Time Understanding (RTU). Could you explain how this technology works to profile visitors based off their emotions?

It starts with our patented visual personality quizzes. From these, we can create incredibly rich and accurate personality profiles very quickly.  They’re rich because the combination of images a person picks can be far more insightful than a list of options and require less interpretation than a multiple-choice of words, and they’re accurate because people are incentivized only by wanting to know more about themselves – we find this makes the answers genuine.

From here we use statistical analysis to put people into clearly-defined groups and behavioral inference to scale those groups into what we call Emotive Segments – anonymized groups used for targeted advertising.

Real Time Understanding profiles a website’s audience in real time, is viewed through the WHYanalytics platform and gives the publisher a deeper understanding of their users by demographic, intent, brand preference and personality type.

The Makegood: This is a very novel idea for marketing. What inspired you to create this technology and launch VisualDNA? How do you see it impacting digital advertising?

It was the other way round, really. Our founder Alex Willcock started VisualDNA because he saw the potential in using visual quizzes to understand people and improve their experience of the internet. If people have the means to communicate their intentions online in a way that’s useful for businesses then the benefits to both parties are clear.

Since then we’ve built a successful business around targeted advertising, and see a huge opportunity in emotive segments. It’s a nascent market at the moment but two big trends are in our favor; first, we know brands already use emotive characteristics to define their customers – the appetite is there to move beyond demographics in targeted advertising and second; the wider market for data is growing as programmatic ad buying platforms get more sophisticated and make it easier to use, publishers are recognizing that programmatic is not a threat to their existing revenues and more brand campaigns are becoming automated.

This all adds up to a bigger data market that we’re ready to capitalize on.

The Makegood: “Big data” is a buzzword today. I see you are also launching WHYanalytics. Can you explain what type of data will come from this and how it will benefit both marketers and content creators.

WHYanalytics essentially visualizes a publisher’s audience using our Segments, thereby revealing personality traits with Emotive Segments – as well as demographic, intent and brand preferences.

We’ll analyze their traffic, benchmark it against our database, and play that back to them – all in real time. To choose a segment at random, in any given online population you’d expect a certain proportion of “Creative Contemplators”. We will assess how that segment typically behaves online. We then look at the proportion of users on the publisher’s site exhibiting those characteristics – that’s where the benchmarking takes place. If the size of that group is well over average (say four times as large as you’d expect) then WHYanalytics will tell you that.

The upshot is that you as a publisher now know that your audience comprises a particularly high number of  “Creative Contemplators” than the norm – valuable insight when deciding what to put on that page, whether it’s an ad, product or editorial.

The Makegood: Many users express concern with privacy invasion when it comes to content personalization. How do you see companies such as Visual DNA handling this as Artificial Intelligence programs advance?

Our approach is to be transparent about what data we collect and how we use it, and we look to create value for people in the process. It’s worth emphasizing that the data we collect stays with us and is not sold to anyone else: the segments we create are completely anonymized.

On the wider issue of data ownership, we think people would be better served if they owned their own data and could use it in a way that creates value for them.

Making that happen remains a long-term goal for VisualDNA but neither the technology or the market is ready just yet.

The Makegood: Real Time Understanding seems to be a breakthrough technology with a lot of potential. What challenges do you see it facing as it progresses?

In the short-term our challenge is to help the market understand the benefits of data, and specifically the segments that we create out of it. Online advertising is a complicated industry and sometimes even those involved with using data on a day-to-day basis struggle to use it effectively – and on top of that the market is moving so fast.

So the first step for us is making the market aware that there’s now high-quality Emotive Segments available, and helping those in the market for data to use it to make their campaigns more effective. WHYanayltics is a great way to visualize an audience in terms of our segmentation, and we have plans to add new features in coming months that will make that information more actionable and measurable.

But we know that everyone in the entire marketing value chain wants to understand people better online – the building blocks are now in place and we’re really excited about the potential.

The Makegood: Thank you, Ian.

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Some new features for WHYanalytics – and a word of thanks to our 270 beta partners

I’m the lead product manager for the suite of WHY products, VisualDNA’s new audience visualisation tools for publishers, advertisers, e-commerce companies and agencies.

Since launching the first beta of WHYanalytics in June, we’ve made great progress in developing the tools with more than 270 companies currently signed-up and using the product: traditional news publishers, online retailers and services businesses large and small from across the globe.

In particular the response from retailers has been fantastic.  Since we have the technology to profile any site visitor in real-time, it’s possible for us to power an automated, personalised, shopping experience through our API.

For example, if you know any given visitor to your website is in-market for a holiday, and/or is a relatively conservative spender a retailer can give prominence to products that suit that individual.  We’re a way off being able to offer this as an off-the-shelf feature in WHYanalytics but we’re running some tests with a major UK supermarket retailer to see how this could work in practice.

As this is our first analytics product, we’ve experienced a very steep learning curve and while in many aspects we were technically ready to scale the beta programme, we have struggled to meet the increasing demand on the customer support front. As a result we’re currently expanding our team in order to streamline how we consolidate feedback and feed it into the product-development process. To those of you who have been involved, a big thanks.

The feedback we have received so far has been extremely valuable and is behind some of the key changes we’ve made to the product in recent weeks;

  • Segment reaches are a useful metric for seeing what the composition of your site is. We’ve now also introduced indices – metric highlighting uniqueness of a segment for a particular site or a section compared to a base – the entire universe of VisualDNA profiles.  Moving forwards, we’re planning to expand this functionality to allow website owners to choose a base of their liking whether it is a section of their site or an entire industry sector.
  • Another key change is a new look of the Snapshot view.  The ‘widgetised’ dashboard allows us to present more data points at glance (demographics, intent and brand preferences as well as our emotive segmentation). This was a very important first step in an attempt to make the page adaptable for various screen sizes but also in preparation for making it customisable on per user basis.
  • And finally, we’ve made the signup process much smoother; after completing a simple one-step registration process you can delve right into the tool – no need to go back to your email to click on a confirmation link. Yes, we hate those as well!

We’re big advocates of single login and hence anyone who has already signed up and deployed code for WHYanalytics will be able to use the same sign in to access WHYcampaigns – a new product coming soon that will allows advertisers and agencies to see who’s interacting with their campaign from the moment an ad is shown on the network through to conversion.

Another product – WHYplanner – also coming soon, will allows agencies and planners to browse and discover VisualDNA audience segments and combine these to create a target audience in an answer to a campaign brief. Since no code is required to use this product – that won’t require a sign in at all. Follow us on twitter or this blog using Feedburner RSS or email for updates.

Finally – thanks to all our beta partners for your ongoing support and for helping us to build a better product. If you’ve not yet signed up and want to try it out simply visit http://why.visualdna.com. It’s free.

How to use data to optimise targeted advertising campaigns

This article originally appeared on The Drum on 13 September.

The growth of big data has seen a rapid increase in the use of data segments to power targeted advertising, the science of reaching anonymised niche groups of people defined by common characteristics.

VisualDNA uses the first-party, self-declared data we generate from visual personality quizzes to create high-quality Demographic, Brand Preference, Purchase Intent and, now, Emotive segments that power targeted advertising.

With multiple factors affecting the performance of a campaign much like effective media buying effective data buying is something of an art form. Programmatic? Not really: data optimisation is done manually, and by smart people. Our top tips for using data segments successfully:

1. Beware of ad-blindness! Don’t over-impress…

A car brand running a campaign of 50 million impressions will likely want to reach people in-market for a car. In theory, a segment such as “Car Buyers” would yield great results, but if that segment comprises just 500,000 people then each user would see 100 impressions a month. Not good.

Over-targeting an audience that’s too small bombards your audience – reducing conversions and increasing CPAs per impression. So define the total number of impressions served and work back. If an average of 15 monthly impressions per user is about optimal on a cost-per-impression/ conversion basis, then a 500,000-strong segment is only enough for a reach of 7.5m impressions.

A run-of-network to make up the remaining 42.5m impressions means you’re reaching a wider audience, reducing the average data and media cost per impression –plus you can test the performance of the data. Data can dramatically increase conversions, but you won’t hit that uplift and justify your data spend if you over-impress.

Trading desks know this of course but thinking about data at the media planning stage helps them buy better segments. The IAB’s Data Usage and Control Primer is a great intro for anyone using data as part of a campaign.

2. Use analytics tools to define and build your audience

At face value, choosing segments should be straightforward.  Quality data, demographics from trusted providers such as Experian, and intent data from sector specialists should perform. Descriptive VisualDNA segments such as “iOS Preferrer”, or Emotive segments, built from self-declared data collected through our quizzes, are also obvious ways to build an audience.

But not all segments are self-evident. Audi, for example, might intuitively buy an “Audi Preferrer” segment from one provider, but it might be that a “Luxury Holiday Preferrer” segment from another provider is a better fit for their brand.  Adding such a segment to a campaign would increase the size of the targeted audience, improving the performance of a campaign

In addition to industry-standard audience insight tools such as Quantcast, WHYanalytics profiles a website’s audience to show which VisualDNA segments rank most highly – powerful new insight for publishers, ecommerce, advertisers, media agencies and brands.

Such tools help publishers meet high-value brand campaign briefs or identify niche audiences while, in ecommerce, online stores can personalise around personality. Plus agencies can pick the best segments to build a targeted brand advertising campaign.

3. Set the right metrics, test the data & monitor throughout.

What does success look like? A Victoria’s Secret creative showing an attractive woman sat on a beach may attract a disproportionally high number of clicks from men, suggesting that male-skewed segments should be used, at the expense of other female-oriented segments…

So it’s about conversions above clicks but track both with a short, un-targeted, “Discovery Phase” at the start of the campaign against each creative.  Whether it’s a direct response or brand campaign, by analysing the ad (impression beacon) and conversion page (conversion pixel) we can tell you which segments are responding. From here you can optimise around the right segments, and have a benchmark from which to measure performance.

We’ve created WHYanalytics to help publishers, ecommerce websites, advertisers, brands and agencies better understand VisualDNA data, and use it to create a better-personalised experience for their audience – we’re working with our beta partners to build more features and functionality that will make it easy to track. Free to use and easy to deploy, try it out at why.visualdna.com/analytics