Household Panel Data Metrics
In the last several posts, we surveyed the innovative things we can do with the Nielsen, IRI, SPINS and other syndicated data that you’ve been buying for years. Now it’s time to turn inward and look at household panel data.
Few CPG companies extensively mine their own panel data, instead relying on syndicated data almost exclusively. These companies focus on the fact that syndicated data gives them a view of the entire market, and not just their own shoppers. But, when panel data is used, it’s often too generic (buyer count, purchase frequency, etc.), to be useful. There’s rarely a story or strategic information on which they can act.
Successfully wielding panel data requires a core understanding of the basics and a certain amount of creativity. Let’s begin with loyalty shoppers.
Buyer Loyalty Metrics
The first loyalty measure we're going to talk about can be mined from your panel data. It’s not a provided measure; you have to create it. But with a bit of legwork, it’s readily attainable data that’s actually quite useful.
It’s called Purchases per Repeater, and it can help you determine the extent to which you have a franchise that inspires repeat hardcore loyalty in its customer base. Here’s the equation:
PB is Purchases per Buyer, and r represents your Repeat Rate, which is the percentage of buyers making two or more purchases.
TABS Analytics once worked with a sports fitness provider, and even though most buyers of their nutrition bar were not repeat customers, the PR helped us identify the existence of a small group of “superfans” who bought over and over again.
When industry people talk about loyalty, it’s usually exclusively in reference to a metric called Share of Requirements (SOR). Of those who are buying in the category for a particular brand, it represents the percentage of the category requirements allocated to that brand. Look at the following chart:
The SOR for dollars and units are at the top. 42% of Pine Mountain buyers spend their category dollars on that brand. However, people tend to ignore the question of brand loyalty after examining that one figure. There’s significantly more to brand loyalty than that, and you need to look at it from various perspectives to get a true sense of customer loyalty.
At first glance, the $ SOR of Pine Mountain show it at a slight disadvantage to Duraflame, but still within striking range. The Unit SOR tells a somewhat different story, with only 29% of the category going to Pine Mountain. This could be easily forgiven, given the stronger $ SOR.
But Pine Mountain also has a weaker showing in Repeat Rate, at only 25%. Of course, the repeat rate can be greatly influenced by distribution, which we discovered to be spotty in the case of Pine Mountain.
It’s important to note that certain brands, particularly the more innovative ones, tend to draw customers away from marginal category buyers. Some brands tend to show poor loyalty rates precisely because they’re bringing in these marginal buyers. To keep such brands from being unfairly dismissed, we used an adjustment called Measured Repeat, which takes the repeat rate for the brand and divides it by the repeat rate for the category.
Next is a basic measure called Purchases per Buyer, where Pine Mountain is also under performing compared to its competitors. But finally, we arrive at the first metric we discussed, Purchases per Repeater. Aside from the SOR, it shows the most extreme deviation between the brands in our example, with Pine Mountain again being the weakest.
In cases where a brand is weak on this measure, it is almost certainly due to a problem with the product. You can’t explain it away with distribution differences, because it includes only those customers who have already found you. As they’ve already bought it once, it’s also difficult to explain away with price differences, unless it’s a price/value issue, where it didn’t deliver the expected quality. We’ve actually managed to find and flag several product quality issues, merely by looking at this measure. Conversely, a strong showing in this measure can reveal the existence of previously unknown brand strengths.
Retailers often view equity as synonymous with loyalty, but there’s a crucial difference. Loyalty is the degree to which a buyer of a specific brand continues to buy that brand rather than other, similar brands. Equity is the degree to which a brand can successfully migrate their consumers in an established category into new categories where a brand competes.
Loyalty = “I like Brand X hair gel. It gives me the hold I need without flaking. I think I’ll stick with it.”
Equity = “I like Brand X hair gel. I see that it also makes shampoo and conditioner. I’ll give them a try.”
We consistently find that, although many brands have high brand loyalty, very few have brand equity. A lot of manufacturers confuse the two, leading them to ill-advisedly enter markets due to loyalty in a different category.
One famous example is Gillette, which enjoys off-the-charts loyalty in their non-disposable razor blade refills. They have made numerous attempts to expand into categories such as hair care, with limited success.
Fortunately, to help avoid costly mistakes, we can measure equity. Look at the chart below:
We’re looking at three different cosmetics brands here: Physician’s Formula, Revlon and Maybelline. Maybelline enjoys incredible loyalty in eye cosmetics; it has the Great Lash franchise. But does it have equity?
Each of the brands also makes face care products. Let’s look at how well they migrate their buyers over to face care. As you can see, 29% of Physician’s Formula eye cosmetic buyers also buy its face care products. They only draw 9% of Revlon eye care buyers, and a mere 5% of Maybelline eye care customers. We therefore see a very high disposition to migrate to another category. The Equity Index states that Physician’s Formula eye care buyers are four times more likely to buy its face care products than another brand.
Looking at Revlon, we see a slight disposition, 22% vs. 17% and 11% in the other brands. But looking at Maybelline (again, the single most popular brand of eye care), we see that the Maybelline name carries very little weight in the world of face care. A Maybelline eye care buyer is no more likely to buy Maybelline face care than an eye cosmetic buyer of any other brand.
In summary, when analyzing the success of a brand’s major foray into a new category, this measure is a great benchmark as to whether or not the launch worked. It’s also a good metric for looking at a brand’s previous crossover endeavors to help us determine if a new category launch would be a good idea. Did it fare poorly, even in a category that’s more in-line with its core strength than the category you’re currently exploring? This is a strong signal that it may be time to reassess.
Buyer Conversion and Migration
Now it's time to address one of the most misused pieces of panel data, Buyer Conversion. In truth, it’s not a particularly reliable metric. TABS Analytics has managed to improve upon with this measure with something we call Buyer Conversion Efficiency. Look at the following chart:
In this chart, we’re comparing Walmart and Target. Dollars are much higher at Walmart. Next is a measure called Audience Potential, which is the likelihood of the store’s shoppers to buy in this particular category. With the percentage of households buying, we can really see the market penetration of Walmart.
The standard definition of Buyer Conversion is the percentage of households buying, divided by the Audience Potential. In the case of Walmart, the equation works like this:
Going by this definition, Target has a conversion problem in every single category that they stock. Its buyer conversion rate at 17% is less than half of Wal-Mart’s. But why is this? If we look at the Retail Trips per Buyer measure, it’s easy to see that Walmart has nearly doubled the opportunity to convert its buyers.
So, if we take the Buyer Conversion, and divide it by the number of Retail Trips per Buyer, we get a very different assessment. This is what we mean by Buyer Conversion Efficiency, and we can now see that Target’s conversion performance is actually slightly ahead of Walmart.
When assessing buyer opportunity, it’s clear that conversion isn’t the bottleneck, but rather visitor frequency. Under Purchases per Buyer, we see that Walmart is getting people through its doors significantly more often. Once Target successfully gets them through the door, conversion ceases to be an issue.
Let us move on to the Retailer Migration Index, another oft-misinterpreted measure. This chart represents a traditional Leakage Tree for Target:
It assesses the spectrum of other retail outlets, and how Target might lose customers to them. One of the problems with this metric is the philosophy that you have an inalienable right to keep all your shoppers, which isn’t a very healthy orientation to have. People will always actively shop across outlets.
Going by this metric, Walmart will always be the biggest threat, indicated by that 37% migration to Mass retail outlets. But let us try something else in order to provide a bit of context.
Taking into account Wal-Mart’s market control (based on HBC panel data), we see that “expected loss” differs a bit from reality:
Losses at Walmart weren’t quite as bad as expected, but look at the Food outlet. The actual losses there are worrisome, and Target’s exclusive competitive focus on Walmart may be a bit misplaced. Food is the channel of which it needs a better understanding.
If Target's sole competitor were Walmart, the strategy would be to focus on price-cutting initiatives. But because it's losing a much greater than expected share to Food outlets, Target needs to examine an alternate strategy, as price clearly isn’t the main motivator.
That does it for our discussion on CPG household panel data metrics. Hopefully, we've illustrated the power behind several measures that are not readily known or available unless you dig a bit further to calculate them. Get the second New Metrics guide covering panel and survey data by clicking on the banner below.
In our next post, we’ll examine the importance of Survey data, an inexpensive and often underutilized way of getting to the heart of your buyers’ motivations.
Source: TABS Analytics Webinar Series “New Business Metrics with the Same Old Marketing Data”; August 30, 2013