Category Archives: Sales

Dynamic Clustering, a new ad targeting methodology

This article is a summary of my proposed methodology to targeting web users with relevant ads, but without intrusion, e.g. without third-party cookies. The original version, including a broader depiction of the context, is a longer post in French : “Le régime sans cookies, le nouvel âge du ciblage sur internet

Basically, as related in many articles, including previous posts on this blog (see for instance “Giving up cookies for a new internet… The third age of targeting is at your door“), the usage of third-party cookies is bond to dwindle, if not to disappear. Some solutions have already been submitted, such as fingerprinting (still intrusive though) or unique identifiers (but too much linked to the major existing internet companies).

So, we need a non-intrusive contextual targeting solution, which takes privacy protection into account. This is the core idea of my proposed solution, e.g. “dynamic clustering“.

Ciblage_Données

How does it work?

  1. Based on ISP and/or Operator log files, browsing data will be collected and anonymized (for instance through a double-anonymization filter) so as to protect one’s user privacy; Anonymous
  2. Files will be cleaned (“noise-reduction” processes), and organized at various categorization levels, so as to generate multiple dimensions, all of them rich and flexible. This will allow to create “outlined profiles” for each unique anonymous user; Categorization
  3. Using these dimensions, clusters will be generated, made of users with similar usage behaviors, based on each advertiser’s hypothesis, creating hence an infinite number of target groups, whose volatility is an asset, as it will always cover the client issue of the given moment.

ClusterSo yes, the no-cookie diet is possible… And it goes along with a more virtuous targeting of the internet users…

Convinced by this new diet? Willing to collaborate to the recipe development? Let’s meet!

Data Elicitation in three steps (3/3): Data Analytics

Today is wrap-up time. Before dealing with practical use cases and comment on data-related news, I shall conclude my introduction series with its third part,analytics. And now, you have your data set, well-organized (patterned) and trained (enriched), ready to go. You only need to find the proper tactics and strategy to reach your goal, i.e. get them to talk and find the solution to your issues or validate your assumptions.

How is this analytical work? Sometimes complicated. Often critical. Always consuming.

Let us first ask ourselves a few questions:

  • Is my software adapted and scaled for reaching my business goals? → needless to say, although… a nice database requires an efficient interface
  • What type of tools and techniques may I use for getting the best out of them? → data drill down and funnel analysis is not equal to random search
  • What are the patterns within my data? → how to reach global conclusions from exemplary data excerpts
  • By the way, do I know why I am storing so much data? → small data sets are often more useful than big (fat and unformed) data mounds

In fact, and even though one may build databases hundreds of ways, using very different tools and techniques, there are only two ways to analyze data, Mine Digging, and Stone Cutting.

1. Mine Digging

Mine Digging is the typical initial Big Data work. Barren ground, nothing valuable to be seen, there may be something worth digging, but you are not even sure… This is often what Big Data offers at first look. Actually, a well-seasoned researcher will find out if something interesting is hidden in the data, like an experienced geologist deciphering the ground would guess if any stone could be buried in there. Still, excavating the data to reach more promising levels is a lot of work, often named “drill down”, an interesting parallel to the mining work… And something will be found for sure, but more often stones of lesser value, a Koh-i-noor is not always there, unfortunately… This huge excavating work I have named Mine Digging.

Retailer-vs-Competition analysisI have taken an example from a previous Market Research experience to illustrate this.

Digging in the data is cumbersome. No question. You seek for hours for a valid analysis angle before finding one. And then suddenly, something. A story to tell, a recommendation to share, a result of some previously taken action to validate. A gem in the dark.

The attached chart shows the example of a given retailer (A) compared to its competitors in a specific catchment area. Its underperformance being so blatant, it was heading unfortunately to a shutdown; however, we found additional data, and suggested an assortment reshuffle and a store redesign which finally helped retailer (A) to catch up with town average in less than 18 months.

This example shows how one may drill data down to the lowest level (shop/town) from a full retailer panel, so as to find meaningful insights

2. Stone Cutting

Stone Cutting has more to do with on-going analytics, especially those taken from existing software, be they for digital analytics, data mining, or even semantic search. In this respect, one already has some raw material in hands, but its cutting is depending on current conditions and client wishes… The analytic work, in that respect, is to find out how to carve the stone and give it the best shape to maximize its value. This refinery work is what I name Stone Cutting.

Click-Rate analysisI have chosen an example from the web analytics world to illustrate this.

When optimizing an e-commerce website, one very quickly knows the type of action that triggers improved conversion; the analytics will then “only” provide some marginal information, e.g. what this specific campaign has brought to the company’s business, its ROI, its new visitors and buyers. Very important for the business, for sure. Vital even.

The attached example shows for instance that the efficiency of the banner-ad impression (click-rate per impression at more or less 5 per thousand) is stable up to 5 impressions, beyond this point, additional impressions are less efficient.

Information straight to the point with results also immediately actionable.

So two ways of analyzing data, one requiring heavy work and a lot of patience, the other one rather using brains and existing patterns, but both are necessary for efficient analytics, from breakthrough discoveries to fine-tuned reporting. Diggers in the South-African mines and cutters in the Antwerp jewelry shops are two very different populations, but both of them are necessary to create a diamond ring. For data analytics alike, a global in-depth knowledge of the analytical process is required, so as to offer the best of consultancy. So, let me remind you , my dual experience  in marketing and operations is a real nugget that you can get easily either on a part-time or full-time basis.

Data Elicitation : my professional new start in 2014

As you could read it last week in Revival of a digital non-nativeI am now more qualified than ever in Digital Analytics, ready to write the first pages of my professional new start.

It has been very nice to receive a high number of positive feedback and to state the concrete interest my last post has aroused. As announced last week, I shall now elaborate what I am at. This post defines the core business of Data Elicitation. Further ones (in a series of 3) will give much more details about specific contributions closely linked with my own proficiency, and answering concerns expressed by marketers, namely through this study by StrongView (2014 Marketing Survey). Key areas are:

1. Data patterning

The original sin of Big Data is its formlessness. So as to be able to use these data, and get the best out of them, one must organize and structure them first. This is what patterning is about.

Or course, your engineers will claim they have built the best database ever, and that it should answer any question you have. This may be true. Or not. Actually, many databases are built under technical constraints, with very little regard to usage and user experience, let alone to marketing and strategy needs. My own experience testifies that an efficient use of data is first built upon a correct understanding of the client requests, i.e. that the initial step is not drawing the plan, but thinking about how it would best fit its goal. This always has been a key driver of my action, especially when building up various new services in the marketing information business. I am a resolute data patterner.

2. Data enrichment

Your data are rich, especially if you can use them easily thanks to an appropriate patterning. But they certainly can be richer. Much richer. And most certainly not at high costs. This is what enrichment is all about.

You may have tons of data, and still this may not fit your purposes. Or on the contrary have small databases, but with a very high (and maybe hidden) value. And enriching is not only adding external information, it is also deriving, translating, cross-checking existing sources. Market Research companies used to name this data enrichment process “coding the dictionary”, a phrase showing the vastness and complexity of this process.  Getting the relevance out of the data is definitely a precious skill, and one of my own key proficiencies.

3. Data analytics

Now, your data are accessible and usable. Fine. And what next? Getting the best out of your data is not always easy, as the meaning may either be blurred or the solution to the problem lost as a needle in a haystack. This is what analytics are about.

And once your data are fit for use, you need to find the proper tactics and strategy to reach your goal, i.e. get them to talk and find the solution to your issues or validate your assumptions. This requires a fair analytic technique, but also a good flair for identifying where the gems are hidden… In this respect, as a seasoned market research expert with a solid digital background, I shall help you identify where to dig to get the best out of your data.

So in the end, this whole process of patterning, enriching and analyzing data may be summarized under one single word: elicitation. I have chosen Data Elicitation as an umbrella designation for running all these processes and bringing them together as a service.

On a practical level, my door remains open to any CEO who would require my exclusive working force to set up their data marketing corporate strategy (e.g. hire me). Still, the current market conditions, notably in France, imply that flexibility is key, especially in the light of project-driven action. This is why I offer my (invaluable) resource also as a contractor. So? Drown in data? Or searching them desperately? And in need of elicitation? Let us keep in touch and let 2014 be the year for your ultimate data elicitation!