CPG companies spend a lot of advertising dollars to build and maintain their brands both in traditional media and increasingly in the online world. Directly measuring the effectiveness of such brand building efforts is challenging and companies default on sales volume as a proxy metric, knowing fully well that there are myriad other effects which influence sales. Other than that, survey based brand recall studies are also used to indirectly determine the effectiveness of advertising.

Online channel lends itself very well to measurement and CPG companies can exploit that fact. CPG companies should geographically segment (by DMA) their website traffic and correlate it with that particular market’s mass media spend to determine whether advertising is creating interest in their products. Media spend and mass market campaigns have a correlation with greater online activity and web analytics provides a way to measure the effect. The online activity metric also provides a real time campaign tracking ability to companies. Proper setup of test and control markets will provide insights into effectiveness of various forms of media spend (online only, online and print, only television etc.) for different geographies.

Setting up such a capability requires, a good understanding of baseline metrics of the website and also a solution which will help to link the IP address of the website visitor to a particular DMA.

While this method is also not full proof as it tries to measure effectiveness in terms of activity on the online channel (that too only on company’s own website), it provides a view from a channel where at present, activity is not systematically measured and very seldom correlated with cross channel initiatives and overall business metrics.

Jim Novo, who authors an insightful marketing blog, rightly identifies that PRIZM clusters (or other geo-demographic segmentation systems) are not as predictive as a behavioral data for desired ‘action’ in the online world.

e-commerce folks are usually looking for behavior from customers, and the fact demographics are not generally predictive of behavior by themselves

Segmentation and targeting are different and PRIZM is fundamentally not a targeting tool. Even in the world of direct mail where rich behavioral data from website is not available; it is rarely used as the sole targeting criterion.

However, geo-demographic segmentation systems like PRIZM, have some benefits for which web analysts should consider using them. Here are four reasons for which we have used PRIZM in an online setting:

1. Customer data is available
When customers login to your website and it is possible to link them to their PRIZM code, you have one more variable to put into your targeting models and an additional dimension of information on your customer.

2. Tie back to non-online world
In most organizations, web analytics is another marketing silo, and senior executives do not yet understand how to relate their online and offline customers/visitors with their media buying strategy, direct mail strategy etc.. A segmentation scheme like PRIZM plays the role of a standard translation engine for the various marketing organizations to describe a customer. Identifying your online visitors using PRIZM helps your marketing department create a holistic view of the customer.

3. Hypotheses to identify content gaps
If you are a content-based site, then having PRIZM segment of your visitors associated with its rich detail on media consumption and lifestyle interests, will help develop hypotheses on the existing content gaps on the website. You will need to augment it with good market research and do rigorous testing to identify what really works.

4. Monetizing the site
Advertisers understand PRIZM – so if you have advertising inventory, then you should consider segmenting the visitors based on it.

PS: PRIZM is one of the major “geo-demographic segmentation” schemes available in the market and we have used the two terms interchangeably in this post

A wireless service provider wants to determine where it should open new stores to attract customers. A paper products company wants to determine whether it is setting the right quotas for its sales people responsible for generating business within their geographies

Companies are often interested in determining the potential size of their market in a particular geography or for a specific product to better manage their sales, distribution and marketing resources. This market sizing problem broadly falls into two categories: whether the customer is a business or a consumer.

The analytical approach to determine addressable markets happens to be very similar. It starts with identifying a very rich data source for the universe of customers, and then applying a layer of filters (e.g. companies with >100 employees, or families with children) based on the specific business needs, to define the target market. This is followed by a estimating the amount the target customer will spend on the service/product – to calculate the potential market size.

Here are two papers which clearly articulate the analytics process, and are a must read before you start any market sizing activity. The first one is for those who sell to businesses and the second one is for the consumer market.

What once was a good ole’ boy sport that relied on tribal knowledge buried deep inside a crew chiefs’ head is becoming a hi-tech, data rich, and analysis driven sport. More and more teams, crew chiefs, engineers, and even pit crews rely heavily on granular data elements that are used to feed complex models to extract the slightest bit of knowledge that can be used to save thousandths of a second on the track or in the pit. The sport of NASCAR is no longer defined by car lengths that won the race rather it’s becoming a sport defined by hundredths and in some cases thousandths of a second that decides between the winner and the first loser, a.k.a. “second place”.

After having an interesting and thought provoking conversation with Ken Brown, former engineering director at Dale Earnhardt Inc. and lead engineer for GM’s Corvette Racing program, I found myself drawing parallelisms between what I do in marketing science and statistics at Diamond for our clients and what NASCAR teams are doing to extract value from information. Not in the ROI sense but in the thousandths of a second sense. However, this in the end does translate into ROI for the thousands of sponsors that make the sport go round and round, no pun intended!

According to Ken, granular data elements such as wedge, nose weight, spring rates, tire pressures, camber, and gear ratios (just to name a few), all play a critical role in determining the how good a car will perform at a particular race. However, the key decisions are often made long before the car arrives at the race track. Essentially through track testing, wind tunnels, shaker rigs, and simulation exercises, the engineer and crew chief can decide which and how much to change each variable to make their car the best on the track. In marketing science I call these things covariates!

So how are teams doing this? Although the details of the process are unique to each team, the general process that goes into setting building and setting up a stock car is as follows:

1. Know the track (flat track, road course, banked)

2. Define/identify the significant set up parameters (or covariates) relevant to that track

2. Gather test data (prior race data, testing, wind tunnel, etc) and validate its quality

3. Analyze preliminary data via data visualization techniques (histograms, pareto charts, and cross-tabs)

4. Build models (e.g., DOE, Pareto Analysis)

5. Analyze results of simulation techniques

6. Set-up car for the race

7. Test and refine!

This process that is followed by the top teams is not unlike the process we follow in our information and analytics practice at Diamond when trying to identify the key variables that will attract and retain the customers or segments a business is going after. Moreover, the quality of the testing data is one of the most critical components to the entire process.

So the next time you see a professional stock car race, it’s important to know that before there was the driver in victory lane, there was a data geek in the background making it all possible.