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


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).


Growth Strategies and Managing Differences in a Global Economy

As more businesses become global companies face challenges in balancing local conditions with economies of scale. The AAA framework by Pankaj Ghemawat is one way to address this challenge. The three A's stand for Adaptation, Aggregation and Arbitrage. Adaptation boosts revenue and market share by maximizing a firm's local presence such as Mc Donald's in India has adapted its menu to suit Indian tastes by providing the McAloo Tikki burger (spicy potato burger). Aggregation standardizes the firm's product or service offerings by grouping together production processes. Apple manufactures its products in China and markets its products in the US.  Arbitrage exploits the differences between regional markets such as call centers in India, factories in China and retail stores in Western Europe.

A firm can choose one of these strategies or a combination. It can also shift strategies at different points in its evolution. IBM started with the adaptation strategy by setting up mini IBMs in target countries and adapting to local needs. In the 1980s it transformed to a regional dependent organization thus shifting to the aggregation strategy. Most recently it shifted to the arbitrage strategy by exploiting wage differentials in India and increasing its headcount in India.

Which globalization option does a firm choose?

In making this decision managers can use the AAA triangle to make a decision. Firms that do a lot of advertising will need to adapt to the local market and lean more towards the adaptation strategy. Those that do a lot of R&D will use the aggregation strategy and firms that are labor intensive will use the arbitrage strategy. Though firms can use a matrix approach and have two strategies in place employing all three has its constraints in terms of limited managerial capacity and a confused culture. It is important to ensure the strategy is a good organizational fit. A firm could also get external support to integrate across borders. IBM has many vendors and joint ventures to help with its R&D and manufacturing. It is also critical to know when not to integrate as this minimizes points of contact and friction. Choosing to use or not use these strategies can help or hinder a  firm's global growth plans.


Business Intelligence=Smarter Decisions for Companies

Forrester Research describes Business Intelligence as a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information to enable more effective strategic, tactical, and operational insights and decision-making. Business Intelligence aims to support better decision making in an organization. And as we know smarter decisions result in better results.

With Business Intelligence, Organizations on real-time can monitor “how are we doing”, “why we are performing in that way” and how should they be doing.

For this post, I describe a case study that gives an example of how to draw insights.

Let's assume company ABC launched a Product A in 2005 and has seen significant growth since launch. The situation here is that ABC is trying to understand the impact of a competitor (Company XYZ) that launched another Product B in same market in Q3’08 and Product A will start losing share to product B. As a first step, we plotted raw data to get some understanding of product A performance in recent years. In chart 1, I have plotted the raw data and to smooth volatility of raw data, I have plotted the 4 week moving average as well.

Some of the findings from this exhibit are as follows:

  • Over last two years, product A has seen steady growth
  • Product A is a seasonal product and sales remain relatively flat till April followed by a steep increase in May in 2008 and 2009. This is consistent with expected use of the product because most consumers use this product in summer and fall
  • For 2010, there was no increase observed in May which is a concern for Company ABC

Since the data is on a weekly basis, I looked at it in a different way. We plotted each year as a separate series for a 52 week period to further investigate findings from above.

On a year-on-year (YoY) basis, 2009 was 23% higher relative to 2008. Year-to-Date (YTD) 2010 is 9% higher relative to the same period in 2009. Essentially there is a wide separation between 2009 and 2008 lines. Till April’10, there was separation between 2009 and 2010 lines but since May’10, 2010 weekly sales are tracking at the 2009 weekly sales.

Based on above analyses for Product A, it was inferred that sales since May’10 have been flat and we have not seen a summer spike in 2010. ABC received similar weekly sales data for competitive Product B from a data vendor and the next step in the process was to analyze the recent trends in Product B.

In similar format to Chart1, Chart 3 below shows the weekly sales for product B since launch in Sep’08.

From Chart 3 above, sales for Product B demonstrate steady increase since launch in Sep’08. Like product A, there was no sharp increase in May’09 and this aligns with competitive intelligence received last year that Company XYZ didn’t have all the promotional material ready by summer 2009. However since May'10, there was a significant increase in sales which is the same time period when sales for Product A didn’t demonstrate seasonal spike similar to historical patterns.

The next step in the process was to compare sales trends between the two products. Plotting the sales trends in same chart would not have provided a meaningful comparison because scales for two products were different. Growth comparison is more relevant when comparing two products with different scales. In this case, we looked at the indexed growth i.e. we compared 4-wk moving average to respective product’s previous year avg. Chart 4 shows the growth comparison between Product A and Product B.

From the above chart, it can be inferred that growth rate for Product B increased significantly in May’10 and Product A remained flat. Based on further discussion, it was found that Competitor XYZ had launched a promotional campaign which increased awareness of Product B among customers. The analysis was further expanded to compare sales between products in different geographies and different customer segments. Based on this analysis, ABC designed a new marketing program to show benefits of Product A were superior to those of Product B. This analysis helped Company ABC understand competitive threat which they were able to successfully blunt with innovating marketing programs.

The above case study is an example of insights that can be drawn from vast data and how these insights then drive decision making. With so many Business Intelligence Platforms currently available, organizations can build performance monitor dashboards metrics on a real-time basis to make smarter business decisions.


Using a Balanced Scrorecard to Drive your Company’s Strategy

A balanced scorecard is used to measure, align and motivate four aspects of a company's performance- financial, customer, internal business processes and learning and growth- with a company's vision and strategy. Measures can be both quantitative and qualitative.  To help explain this concept  I have applied this approach to increase my brand equity through my blog in the image below.

Page 1 of 11

Switch to our mobile site