Shazeeye's Blog Thoughts on User Experience, Technology and Business

30Nov/110

Micromarketing: Location data to better serve your customers – Part 2 of 2

Location data such as using a zip code to find out how much revenue a grocery store can make is critical in your decision to decide if you want to open the store at that location. This is just one example of the powerful potential of micromarketing. Read an earlier post to get the details. Let's look at some more examples of how micromarketing can be used in defining marketing campaigns and identifying sales trends.

Identifying Marketing Campaigns based on Market Potential: Market potential is the estimated maximum sales revenue of a product during a certain time period. MapInfo Professional visually depicts the market potential of households who spend more than $150 per week on groceries for each block group (group of adjacent zip codes) in Orange County. The software also gives details on which customer segment will most likely contribute to the sales at the grocery store. For details on customer segments based on PRIZM groups read the earlier post. We see that White-Collar Suburbia have the highest market potential (count* penetration) of 21.1% and hence will be the target of a marketing campaign. This group is well described and is very specific so a direct mail ad campaign is suitable. As this group is family centric and enjoys a healthy and busy (both parents work) lifestyle we can tailor the campaigns to emphasize healthy foods and easy to make dishes that brings the family together. We can also identify the market potential by block group so say if Block X has high market potential we will place a billboard in that area to target customers. We could also use coupons to entice the White-Collar Suburbia that live outside the trade area (area where customers that visit the store reside - usually a 5 minute radius for a grocery store) of the grocery store to visit the store.

Using Point of Sale Data (data collected at cash registers) to Identify Sales Trends: AC Nielsen collects a lot of data from grocery stores and can show sales trends based on customer locations (zip codes). As seen in the image below we see market share and sales over a year for 2 brands of cranberry drink - Ocean Spray and Coca Cola.

For Ocean Spray we see that within a retailer’s trade area the retailer’s total market share for Ocean Spray’s SS Cranberry Drink is 38.6%, a decrease of 4.3 points from last year. This means that the retailer sells 38.6% of this brand SS Cranberry drinks in this trade area. When we look at the Total Sales we see that the retailer’s sales is down 14% while the remaining market increased by  2.9%.  This means its sales decreased by 14% or people could be going to another retailer with a better marketing campaign (possibly a discount) for this drink in the trade area. The total sales were $700,000+ which is significant. Thus this drink could be a cash cow (based on BCG classification) for the retailer with the right marketing campaign. Plus, the sales for Ocean Spray or the remaining market increased by 2.9% though the overall trend for sales of ocean spray was slightly down by 3.6%.

For Coca Cola within a retailer’s trade area the retailer’s total market share for Coca Cola’s SS Cranberry drink is 23.6%, a decrease of 14.9 points from last year. This means that the retailer sells 23.6% of this brand drink in this trade area. When we look at the Total Sales we see that the retailer’s sales is down 62.3% while the remaining market decreased by  23.5%.  Thus this drink is a dog for the retailer and should be dropped as its market share is less than 35% and its total sales % change is less than 5%. Plus, overall sales were $1450  which is nearly insignificant (less than 1k is insignificant).

30Nov/110

Micromarketing: Location data to better serve your customers – Part 1 of 2

Location data such as using a zip code to find out how much revenue a grocery store can make is critical in your decision to decide if you want to open the store at that location. This is just one example of the powerful potential of micromarketing. Let's go through an example of using location data to open a grocery store in Orange County. We will be using SRC's Allocate to help analyze the location data and MapInfo Professional to map the data.

Mapping propensity and density to determine revenue potential of the store: As we see in the images below, we use SRC's Allocate to determine the revenue potential of  a grocery store in Orange County (OC). We choose the retail store option as the input variable and the dollar per store as the output using the software. Data is also available for furniture stores, sports stores, etc. After the variables are input a map is produced (below) which can be interpreted as follows. For Orange County, the average grocery expenditure per house hold per month (propensity) across a block group (a group of zip codes) where darker green shades indicate  higher expenditure for groceries per household per month is approximately $5800-$16900/month for the darkest or most expensive parts. The hashed region shows total dollars spent for groceries per square mile per month in Orange County (expenditure density). The darkest hashed regions indicate people in OC spend a total of $18,000,000 to $103,000,000 per month on groceries. This data helps you determine if the revenue potential is close to what you expect and can help compute your approximate profit given all the expenses you will incur. It also helps you compare revenue potential across different locations to help you determine the ideal/optimal location for you.

Choosing a Store Location by Mapping Competitors Location Data: Using the Yellow pages we identify the zip codes of the competitors. For this example - a grocery store - let's assume it's Trader Joe's and Whole Foods. We identified 19 Trader Joe's and 2 Whole Foods store in the OC area and mapped their trade areas (area from where customers visit the store - usually a 5 minute radius for a grocery store) using the software.  The blue areas represent the Trader Joe's and the red and fluorescent green represent Whole Foods. This is mapped on the propensity and density map from above using MapInfo Professional. With this information we choose a location (in yellow) that is far from competitors and has good propensity and density. You will also check for magnet stores, customer demographics and traffic (info in next paragraph) and ensure that the information provided by these parameters will help drive your store's growth. You can also compute the break even demand (average retail demand per square mile) as seen below to inform your decision.

Identifying magnet stores, traffic, customer demographics and trade areas for the new store location: The software helps to draw the trade area for the new store location (for this example a 5 minute radius as seen in black) and can identify the magnet stores or stores that will help pull traffic (for example, drug stores).  It computes traffic - 32,800 cars/day. It also helps define the type of people in the trade area. Types of people are defined by PRIZM clusters (for details check  PRIZM Clusters) and gives you demographics and characteristics of the population you are likely going to attract. According to the report this store will attract 54% of people belonging to the PRIZM cluster defined as White-collar Suburbia. This group can be described as "upscale, college-educated baby boomers living in suburban comfort in expensive new subdivisions". For more details on this segment visit Experian's description. Now that you have such a wealth of information on your customers you can tailor your marketing message as well as grocery needs to better suit them.


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