How much of a market is up for grabs?

In my last blog post I showed you how to use Google search volume to estimate future market share within a product category using Insights for Search, a free Google tool. This time I’ll show you how to estimate how much of a market is composed of consumers looking for a reason to switch brands (i.e., consumers who want you to win them over).

But first a little marketing theory. Most marketers believe that making their customers extremely satisfied is their goal. Customer satisfaction surveys have generally been replaced by one question: On a scale of 1 to 10, how likely are you to recommend us to friends and family? This is known as the net promoter score (see “The One Number You Need to Grow” in Harvard Business Review Dec 2003). Customers near the middle on the net promoter score (i.e., 7-8) are generally your customer only until they can find a vendor or brand they like better. Often when consumers are thinking about making a purchase about which they are not fully satisfied they will update their knowledge of alternatives by making a Google search. Lets look at an example.

Market Share Reporter (MSR) aggregates market share information from a variety sources for the major product categories. One example is the toilet tissue market, shown in Figure 1. Use the instructions in my last blog entry to find the Google search volume for each of the top 5 toilet paper brands over the last 12 months. You will use Google Insights for Search and probably should use settings similar to those in Figure 2. Figure 3 shows results I recently received from that search.

Figure 1. Toilet tissue market share.

Figure 2. Recommended Insights for Search settings.

Figure 3. Sample results.

If you add the market share percentages for the top 5 brands in Figure 1 you might conclude that they have a lock on 76.5% of the market, leaving little opportunity for another toilet tissue manufacturer. However, no matter what market you name its always possible to estimate the not-entirely-satisfied part of the market by checking the search volume for a phrase like “best toilet paper,” or “best tires” or “best NYC barber.” Note the quotes around the example search phrases. Using quotes around your search phrase will ensure your results are not inflated by search phrases that happen to contain those words in a different order, with a different meaning. What’s the significance of knowing that the “best toilet paper” search phrase happens more often than 46% of the searches in the Hygiene & Toiletries category? If Charmin’s brand attention of 72 reflects market share of 23.2%, then “best toilet paper” attention may reflect 14.8% (23.2/72 x 46) market share “in play.” A less conservative estimate could be gained by averaging the results of the same calculation for the top 5 brands (41.1%). Even the conservative estimate of 14.8% represents substantial opportunity. If one new brand could capture it all it would grant instant entry into the top brands of a multi-billion dollar market.


Understanding Your Competitive Landscape with Google’s Tools

In Tim Ferriss’ good book The 4-Hour Workweek he describes how to estimate the revenue from any product with the Google Adwords Keyword Tool. I’ve been thinking: what other valuable marketing information can be gained from Google’s free tools? Take the issue of market share, lets say you have an idea for a new niche product but don’t know who might be competing for the same consumer or how much of the market is already served. You can look at the major industry databases like Market Share Reporter (MSR) or Hoovers. However, unless you are thinking about competing in a major product category (e.g., automobiles, toilet paper, luggage) your market won’t be on their radar. A better way is to follow these steps:

  1. Find out who is bidding on Adwords keywords that are closely related to the product you are thinking about offering (i.e., Adwords that you would bid on to get buyers to your website). As Ferriss says: you use the Google Adwords Keyword Tool to find the words that consumers use to find products like yours and then search using those keywords and make a note of which companies come up in the paid search results (top section and right margin).
  2. Now use another Google tool called Insights for Search (Insights)to see how frequently consumers search for the company names found in step 1. Insights only allows you to look at search history for 5 companies at once, but you can assemble a full list by aggregating results from separate sets of 5 different companies. I  argue in the paragraphs to follow that you now have a prediction of future market share.

Take a look at the following example. Here I’m searching the names of motorcycle brands. Note the two boxes, one around the drop-down box containing the word “Motorcycles” and the other around the bar chart. The drop-down box is one of the most useful features of Insights because you can isolate your search to a specific category so to be sure your results really represent what you intend them to. Take for example the search term “harley.” In this situation I want to know the prevalence of searches for Harley-Davidson motorcycles. I know that some people will abbreviate their search term by using “harley,” so I want to capture that search volume in addition to that of the full brand name. The problem is that some “harley” searches have nothing to do with motorcycles, so to be sure I only get the ones for motorcycles I select the Motorcycle category in the drop-down box.

Figure 1. Google Insights for Search

The bar chart is the information I’m looking for. Note how each bar in Figure 1 has a number beside it. If you try this yourself and don’t see a number then sign up for a free Google account and login. The number is a percentile for search frequency. In this case it indicates how often the keyword “harley” is searched for compared to all other searches in the Motorcycle category. The number 70 means that within the Motorcycle category 70% of all other searches happen less often than searches for “harley.”  Why this number is important to marketers is because it is a measure of attention.

Now lets review a little marketing theory. Perhaps the oldest marketing model is the AIDA model of the phases someone goes through when they buy a new product: Attention -> Interest -> Desire -> Action. Market share statistics like those from MSR  in Figure 2 measure Action, the sales that happened in the past. How can you tell what sales will happen in the future? You look at measures of the earlier phases, and right here in Insights we are looking at measures of Attention.

Figure 2. Market share for motorcycle brands.

Two months ago, before the summer riding season started, I used Insights to see where consumer attention was in the motorcycle category. Today I checked it again with results as in Figures 3A and B. If you compare Figures 2 and 3A you can see that pre-season riding attention was predicting a modest increase in market share for Honda, Yamaha and Ducati, seemingly at Harley’s loss. Generally though it seemed that attention was consistent with the 2009 market shares. Now that the season is underway Figure 3B is indicating not only higher attention for all motorcycle brands, but a restoration of Harley to the top dog position. The really interesting observation is the big surge of interest in the European brands, particularly BMW and Ducati. Will this increased attention turn into a transfer of market share? My prediction is “yes,” and probably to the detriment of Yamaha, Kawasaki and Suzuki.

Figure3A and B. Attention on motorcycle brands.

I started this post by saying I would show you a way to get market share data for any category too small to be on MSR’s or Hoover’s radar. Now you can see that I’m telling you a way to get a prediction of future share. But what’s better: knowledge of the past or prediction of the future? My example was for a major category that is monitored by MSR. I used that example because I wanted to show you that there is consistency between MSR and the free Google attention data. Now go do some marketing!

Mapping the top brands fromTwitter tweets

Since the 1970’s marketers have analyzed supermarket scanner data to get insights into brands that compete for the same purchase decision, and brands that get purchased together. Sadly, the range of brands appearing in such shopping baskets is limited to those found on supermarket shelves. However, consumer choice of which top brands to mention in Twitter tweets, part of what is called user-generated content, encodes a broader perspective on brand relationships.

Such relationships are usually portrayed as a map where brands are plotted in a coordinate system such as Figure 1. However, when applied to Twitter mentions such mapping failed to depict brand relationships that made sense. The problem is that mapping techniques place equal emphasis on brands that are seldom mentioned as they do on brands that are often mentioned, creating a distorted map.

Figure 1. Standard map of brands.

A much better rendering of brand proximities occurs when you emphasize relationships between the brands most frequently mentioned by the same people as shown in Figure 2. Note how brands like Microsoft and Bank of America emerged as centers around which their competitors were organized. Marketers can use these insights as brand-building feedback. For example, Bank of America should be very happy that consumers organize the banking industry around them – that’s valuable brand awareness. It may seem odd that Red Bull is connected to the automotive brands instead of the food and beverage brands, until you Google “red bull racing” and see the investment Red Bull has made in associating its brand with Formula One racing under both the Red Bull brand and its Italian monicker Scuderia Torro Rosso. Figure 2 shows that investment has had the desired effect.

Figure 2. Primary linkages between most-mentioned brands.

Figure 3 shows a true map of brand mentions where brands are positioned based on their location being triangulated from the three brands most mentioned by the same people. The heavy connections are the same as those in Figure 2. Marketers can use this map as a source of ideas to take advantage of unexpectedly finding brands close together in consumer perceptions. For example, note the close proximity between Red Bull and Smirnoff indicative of Red Bull’s widespread use as a vodka mixer. Even though controversy has surrounded the pairing since abuse has led to serious injury, it may be that collaboration between Red Bull and Smirnoff could lead to a pre-mixed product with less potential for harm that corners consumer demand.

Figure 3. A true map of brands.