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.


31Oct/110

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.

30Aug/110

Managing Disruptive Innovation

PARC or Palo Alto Research Center, a Xerox Company in Silicon Valley has contributed tremendously to commercial innovation through ethnography. I am a huge advocate of ethnography and PARC pioneered this process of studying human behavior and "hybridized" it with other social science and analytical methods to optimize it for business application - particularly for addressing new opportunities, customers and markets. PARC owns 2500 patents and have created products such as GroupFire (acquired by Google), Inxight (acquired by SAP) and Uppercase (acquired by Microsoft). You can see some of their presentations here. On August 18th I went for a presentation on Managing Disruptive Innovation by Tamara St. Claire, VP of Global Business Development and Head of Commercial Operations.

Tamara spoke about managing disruptive (vs. incremental) innovation, its risks, two case studies and lessons learned.  Incremental innovation happens in existing markets (left column in image on right) while disruptive innovation happens in new markets (right column) and is more challenging to manage. She mentioned three risks in disruptive innovation - technology, market and execution- emphasizing that markets and execution are the most challenging factors to overcome. A further breakdown of the risks are found in the image below. Lack of credibility/experience (includes C level stakeholders), lack of channel (sales/distribution network) and lack (actually the inability to filter through too much) of information are critical risk factors.

The best way to enter a market of disruptive innovation (with existing or new technology) is to start with a minimal viable product (MVP) introduced at the right time and a strong value chain. MVP is a product with a limited set of features that fits the user needs of a niche market. Once the product has gained an audience ideas to gain mass market with added features can be explored. Tamara gave an example of one of PARC's chip packaging technology which was introduced seven years ago but shelved due to bad timing. It was reworked seven years later by partnering with Sun Microsystems and Oracle due to their advances in chip technology. The value chain are a group of activities (see image below- extreme right) that help to bring the product to market. In existing markets best practices help define a path to market entry but in disruptive markets one has to be flexible and shift gears depending on learnings. It is also critical to partner with experts and consultants studying these new markets as well as visit trade shoes and conferences to learn as much as possible. Partnerships are forged to strengthen the chain and build credibility.

Case Study: Printed Electronics Services

PARC developed low cost disposable printed flexible electronic expertise and devices can be applied in health electronics, packaging and biomedicine. When DARPA (Defense Agency) contacted them to develop an early detection solution to prevent brain injury for soldiers they partnered with consultants and experts to expand their printed electronics services for defense applications. They soon realized they couldn't manufacture the films at the scale desired and thus decided to play a connector role (flexibilty to change is key) between materials and manufacturing.  They partnered with Polyera and 2 other manufacturers thus giving up positions in the value chain and concentrating on their strength (network orchestrator). The lessons are outlined in the image on the right where N=1 means that they relied on more than one consultant or expert to help traverse this new territory and in many cases related to disruptive innovation a group of experts help bring together a holistic viewpoint and a superior product. The other lessons were to be flexible to change course, focus on strengths in the value chain and partner in areas of weaknesses.

Case Study: Content-Centric Networking Protocol

PARC developed a communication protocol complementing existing IP infrastructure to reduce the cost of distributing video and other content in IP/TV networks. Content-Centric Networking uses a unique architecture that caches content closest to the users who request it most thus reducing network capital cost and operating expense.  To create this solution PARC collaborated with Van Jacobson, Chief Scientist at Cisco and an IP/TV expert and took it to open source for feedback. They tested this network with the government and early adopters and used feedback to improve the solution to get critical mass. The lessons here were to get the right commitment, gain critical mass and engage user feedback early.

Overall lessons are to use ethnography to understand how people are using your products and thus have a well defined MVP. Disruptive innovation is more about unique business models and integrating technology. As a company expands it is critical to have a portfolio of products ranging from core to next gen products and using a process to manage this innovation can be the difference between success and failure.

28Feb/110

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.

Page 1 of 212

Switch to our mobile site