From data to actionable intelligence
Data are combined to produce INFORMATION, which is a coherent statement of a set of conditions.
"Today our competitor has registered the domain 'Widgetizer.com.' The website does not yet exist."
Information is combined to produce INTELLIGENCE, which is a coherent statement of what was previously unknown based on what is now known.
"Today our competitor was granted a trademark by the USPTO for 'Widgetizer,' concerning 'services related to electroplating,' something the competitor does not currently offer. Therefore, it appears the competitor is planning a widget electroplating service to compete with our widget maintenance services and that this service will have its own website, 'Widgetizer.com'."
Intelligence is combined to produce ACTIONABLE INTELLIGENCE, which is the discovery of an opportunity or threat that can be acted upon.
"From previous information, we know the competitor is launching a new widget electroplating service. Today our competitor has posted employment ads looking for people in 10 cities who have experience electroplating. Based on a typical one-month talent search and three months of training for similar workers in our own company, today's news lets us estimate that the new Widgetizer service will be launched in June. We should pre-empt the threat by offering discounted long-term service contracts now."
The scenario I used above is based on two actual occasions when I was able to give six months' warning of competitors' new service offerings, provide the technical aspects of them, and correctly predict the month it would be launched. Provided below are case studies and actual examples of some of my other research and analyses that provided actionable intelligence. As I discuss on my "About" page, I am a trained intelligence analyst with experience in the military, law enforcement, and business. I have won awards for my work.
Click on a case study title below to jump to the case.
|Anchor Bank: Location for new bank branch in North Metro||
|Minnesota Orchestra: Segmenting their customers||
|Minnesota Orchestra: Bringing the concert to those who won't drive||
|Various clients: Miscellaneous research||
Anchor Bank: Location for new bank branch in North Metro
Anchor Bank wished to expand its presence in the North Metro with a new bank branch. I was given several towns to research and asked to make recommendations. The list of towns was the only guidance I was given. Everything else was left to me. I would conduct this research by myself.
The ideal location would have:
- Little competition.
- Near- and long-term growth in potential business customers.
- Near- and long-term growth in potential consumer customers (especially high income).
- Near a high-traffic area with good visibility from the road.
My first step was to do a map reconnaissance, overlaying it with various information just as I was taught in military intelligence school. To do this, I used MS MapPoint to overlay census and vendor-provided demographics and business information (including competitor locations). In other research, I obtained current and future zoning plan maps, traffic statistics, Department of Transportation plans, and I met with city officials. I drove up and down the territory with a tape recorder making observations as I drove. I walked much of the territory and took large numbers of digital photographs, meticulously logging each photo for location and direction being faced. At certain intersections, I would construct a 360-degree panoramic view by taking a series of photos in a tight circle, combining the photos back in the office (this being in the days before smart phones). At office parks and shopping areas, I recorded the names and types of occupying companies and how many offices were vacant. I found the tallest buildings with roof access and surveyed the surroundings, taking panoramic photos.
Because of the copious amount of information I obtained, I was able to make not only a confident recommendation, but to identify possible sites for real estate investment and future retail locations if certain conditions were met (and estimate when those conditions might occur).
I recommended Blaine: specifically, the intersection of 109th Avenue & Radisson Road.
- This was the crossroads of traffic to and from high-income households, office parks, and shopping centers in the town, both existing and under construction.
- Many new higher-income neighborhoods were under construction nearby.
- Blaine was attractive to upper-income families due to its recreational assets (a PGA-operated golf course and the National Youth Golf Center). It was attractive to businesses because of its municipal airport that could accommodate small private jets.
- Although competitors were present in the town, they were almost entirely located in the aging strip-mall area on the west side of town.
In 2004 (two years after my report), Anchor Bancorp opened a new branch 300 yards from the intersection I had recommended. In 2014, this branch had the third largest market share in the Blaine ZIP code with over $65 million in deposits, beating out long-established competitors in the market. See below for the FDIC deposit report.
Click the graphic at the top of this case study to view various extracts from my report.
Prior to 2005, the Minnesota Orchestra segmented its season ticket buyers (subscribers) only by major product category: Classical, Weekender Pops, Adventures in Music for Families, etc. There was no accounting for specific music tastes or buying habits beyond that.
As a result, New Age music fans were grouped together with Country/Western fans in the segment “Weekender Pops.” This was not conducive to understanding our patrons or how we might sell to them. One-to-one marketing was impossible.
- We needed to capture musical taste as well as product purchasing history.
- We needed to capture recency, frequency, and monetary value.
- We needed to capture price sensitivity and preferred marketing and sales channels.
In 2004, I developed my new segmentation schema and applied it to our customer database. The first use of the new segmentation codes was for the 2005-06 subscription campaign. Their first use for single-concert (non-season ticket) promotions was in the spring of 2005.
Part of this segmentation schema depended on the Orchestra's "Email Club", an opt-in email feature on their website. In the past it had asked for musical preferences based on the broad product categories mentioned earlier. I changed that to specifics: Classical, Jazz, Gospel, Country/Western, New Age, etc. Using the email subscriber's email address, I could tie back the musical preferences to customer households and their purchase history. This was especially useful for new customers whose buying history had yet to reflect all their stated musical tastes.
Here (categorized) are some of the segmentation codes I used:
- Product (e.g., "Classical 12-concert-series buyer 2 seasons ago")
- Sales Channel (e.g., web, box office, mail)
- Marketing Channel (e.g., member of the Email Club or not)
- Musical Taste (e.g., Classical, Gospel, Broadway/American Songbook)
- Price sensitivity (e.g., Discountee, Bleacher, Spender)
Previously, the Orchestra engaged a marketing consulting firm for its season ticket campaign. My work in this area let the Orchestra eliminate that expense. The 2005-2006 season ticket campaign (the first with my segmentation) achieved slightly higher results than the previous season. More significantly, those sales came at greatly lower costs in printing and postage and with fewer discounted offers due to better targeting through my segmentation. This was in addition to the money saved by dropping the vendor in favor of my less expensive (and more successful) services.
Shown above are just a few of these segments. Click the graphic at the top of this case study for a slideshow on how this was (and could have been) used.
Using Microsoft MapPoint, I plotted information about the Minnesota Orchestra's classical concert customers, both its season-ticket buyers (subscribers) and those who only purchased tickets to individual concerts. I overlaid various demographic data from the US Census Bureau and commercial sources. These demographics included household income, spending on entertainment, and presence of children, to name a few. I reported my findings to the Director of Marketing in an informal report.
- I noticed that the area around Shoreview, Vadnais Heights, and Mahtomedi had a high number of single-concert buyers, but almost no subscribers (i.e., season-ticket buyers).
- Cost did not appear to be the issue because this area had a high percentage of households making over $100,000 a year (and this was in 2004).
- By also plotting the distance from Orchestra Hall, I surmised that it was the distance that dissuaded people in this area from becoming season subscribers, a commitment of six to 24 concerts a season).
I recommended that when the Orchestra planned "outreach" concerts around the State, it should always include the Shoreview-Vadnais Heights-Mahtomedi area. Besides having a large receptive audience, there was the possibility of converting some single-ticket buyers into subscribers.
When the next season was planned for later that year, it included a "Close-To-Home" concert in Mahtomedi (at St. Andrew's Church) --- part of the Shoreview-Vadnais Heights-Mahtomedi area I had identified in my report. In later years Mahtomedi would have two concerts per season due to their popularity.
Demographics: "by ZIP code" versus "by Census Tract" (excerpt from a larger report)