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A management analytics database answers all kinds of profit and cost-related questions. It is more complex than a "contact management system" and much simpler than a customer relationship management ("CRM") system. We discuss the functional difference of each of these in our What is a Management Analytics Database? article. |
This discussion focuses upon the "nuts and bolts details " of what is actually stored in a management analytics database -- and the benefits of having all of this data in one place.
WHAT IS IN A MANAGEMENT ANALYTICS DATABASE ?
A management analytics database is a simple computerized collection of at least four types of data essential to making management decisions. The essential types of data are:
- Contacts (people) names
- Customers (company) names
- Purchases (transactions)
- Products (inventory items)
NAMES: This is a list of your customers – names, addresses, account numbers and so forth. For data-driven tools to work best, it is best if you have at least several thousand customer names with addresses, but sometimes fewer will do. Here is a list of the data items that would be appropriate for the “name” file of a simple business-to-business management analytics database:
- Unique Customer or Account Number
- Company Name
- Suite, Building or Unit (or “Address2”)
- Street Number and Street Name (or “Address1”)
- City
- State
- Zipcode (5-digit part)
- Zip-4 (4-digit part)
- Customer Type or Status (to indicate inactive, do not contact, etc.)
TRANSACTIONS: A management analytics database must also store information about each purchase or sale. Otherwise, it would just be a “mailing list”. Usually this kind of information is obtained from some kind of sales, accounting or ticketing system. At minimum, this data needs to include the date of sale and total amount of sale along with the account number or customer ID to link it to the customer. It is best if this list includes sales data for at least two complete years, but even one full year may be enough.
Here is a list of what would typically be appropriate for the “transaction” file of a simple management analytics database. (Sometimes this information is kept in two files, one for "headers" and another for "items.")
- Customer ID or Account Number
- Invoice ID or Number
- Invoice Date
- Quantity or Count of Items Sold
- Total Invoice Amount
- Item Number or Item Description (optional, but helpful)
- Item Price (optional, but helpful)
- Item Cost (optional but very helpful)
CONTACTS: A management analytics database must also store information about the individuals you interact with at each of your customer companies. In business-to-business sales, there are often three or more people at each customer company that you need to know about and keep track of. (In business-to-consumer sales, things are simpler and this information is often stored with the COMPANY information listed above.)
Sometimes we find this information stored in various "contact management" systems. Here is a list of what would typically be appropriate for the “contacts” file of a simple management analytics database:- Contact/Prospect ID
- Company ID or Account Number
- Prefix (Mr., Ms., Dr., Honorable, etc.)
- First Name
- Middle Initial
- Last Name
- Suffix (Jr., M.D., III, etc.)
- Functional title (Manager, Engineer, Buyer, Owner, etc.)
- Preferred Phone
- Customer Type or Status (to flag inactive, Do-Not-Contact, Deceased, etc.)
PROSPECTS: This data usually comes from "mailing lists" provided by your advertising agency or mailing house. Sometimes we also find it stored in various "contact management" systems. At minimum, this data needs to include the description or code for the ways that you have contacted the customers and/or prospects. Here is a list of what would typically be appropriate for the “prospects” file of a simple management analytics database:
- Prospect ID
- Campaign ID or Number
- Mailing or Contact Date
- Source of Name (or List ID)
- Promo or Offer code and/or Description
- Prefix (Mr., Ms., Dr., Honorable, etc.)
- First Name
- Middle Initial
- Last Name
- Suffix (Jr., M.D., III, etc.)
- Functional title (Manager, Engineer, Buyer, Owner, etc.)
- Preferred Phone
- Customer Type or Status (to flag inactive, Do-Not-Contact, Deceased, etc.
PRODUCTS: This data usually comes from your financial accounting or inventory management system. Sometimes we also find it stored in ERP and purchasing systems. At minimum, this data needs to include the description, SKU or PID code for each product that you have sold -- including those that you no longer stock or sell. Besides the SKU or PID, this data needs to include the price, the cost and the unit measure of each item. Here is a list of what would typically be appropriate for the products file of a simple management analytics database:
- Product ID or SKU#
- Product Description
- Product Group
- Product Vendor
- Vendor Product #
- Cost (LIFO or Standard)
- List or "book" price
- Inventory status (obsolete, current, non-stocked, etc.)
HOW DO YOU USE A MANAGEMENT ANALYTICS DATABASE?
Once you have set up a management analytics database, there are five progressive steps to using it . These steps are something like the pyramid shown below. You start at the bottom and ascend, one level at a time, to the top. Each step reveals new ways to boost campaign response, cut costs and increase sales.
The first level (data hygiene) is the easiest to reach (least expensive), usually provides the most benefit per dollar of investment and provides the foundation for the steps that follow. The second level (list processing) requires additional effort (cost and complexity), provides additional benefits and also provides the foundation for the steps that follow. We discuss each of these steps separately below.
The exciting idea behind this approach is that you can start at the bottom to discover fast, easy and inexpensive ways to boost campaign response. These are "low-hanging fruit" opportunities that every company can use. Then, as appropriate for your specific needs, you can take "next steps" of increasing sophistication and complexity. Many companies just starting to use data-driven tools discover that they can reach their profit goals without going all the way to the expense and complexity of "Step 5: Predictive Modeling."
Step 1, Data Hygiene is the process of ensuring that the data stored in the management analytics database are "clean". That is, the data are complete, consistent and correct. This step often takes more time than the rest of the steps combined. Nevertheless, it is essential because the accuracy and validity of all of the discoveries made in subsequent steps depend upon starting with "clean" data. You can learn more about Data Hygiene in Data Hygiene - Database Bullets to Dodge.
Step 2, List Processing does two things: it both reduces costs and improves response rates. It does this by eliminating the duplicate and undeliverable addresses that are in your database. This may seem like a simple thing to do, but it's not. Improperly done, you can accidentally delete valuable names from your customer list or end up sending multiple mailings to the same person. In one case, we found that nearly half of the mailing list was undeliverable! Fixing this problem cut the company's mailing costs in half -- which also had the impact of doubling their response rates. You can learn more about List Processing in List Processing - The Fastest Way to Boost ROI.
Step 3, Profiling, also does two things. First, profiling allows you to test the "sanity" of the data in your management analytics database. This often reveals subtle data problems that can derail accurate campaign tracking. For example, MAG often discovers double-counted customers and confusions between established customers and prospective customers. Such mix-ups can seriously mislead campaign and sales management strategies!
Second, profiling can reveal customer groups that have been overlooked by thinking in terms of an "average customer". These groups often offer additional "low hanging fruit" opportunities to boost sales, response rates and profits. You can learn more about Profile Analysis in Profiling: Seeing Customers in New Ways.
Step 4, Segmentation , consists of two techniques. The first is called Performance Segmentation. It usually uses a well-known tool called "RFM" (Recency-Frequency-Monetary) analysis.
Management Analytics Group has invented an even more effective performance segmentation technique called "RFA" (Recency-Frequency-Average). This novel approach often provides more helpful customer behavior insights than the conventional RFM approach.
Companies just starting to use data-driven marketing tools may find that performance segmentation may be all you need to reach your campaign response and profit goals. You can learn more about Performance Segmentation in Segmentation - Finding Your Best Customers.
A second and more powerful descriptive modeling tool is Dynamic Clustering. It uses powerful statistical tools to assign each of your customers into self-defining groups called "clusters". This is a "dynamic" process, not a "static" process. That is, this technique defines each cluster upon how your customers behave with respect to your company -- not according to some pre-defined arbitrary definition of an imaginary market segment.
Dynamic clustering evaluates many different kinds of behavior -- not just those used for RFM or RFA analysis. This gives you deep insights into who your customers are, how they behave and how to communicate with them more cost-effectively. These insights almost always reveal surprises that boost campaign response, sales and profits. You can learn more in Clustering - A Dynamic Difference.
Step 5, Prediction builds upon all of the previous steps. Predictive tools actually identify which specific customers will respond to an offer -- and to what degree. This knowledge allows you to make precise decisions about what offers to extend, what media to use and how much you can afford to spend in contacting each customer. This tool is the the most sophisticated and powerful of those we have discussed. There are three analysis technques in this category.
The first is Retention Analysis. It is a simple technique for predicting which customers are likely to stop buying from you in the future -- and those who are likely to remain loyal. It is based upon the common-sense idea that customers start drifting away long before they disappear altogether. MAG uses a simple Up-Down-In-Out ("UDIO") analysis to spot the customers who seem to be drifting away. Usually we can identify such customers early enough for retention campaign efforts to be quite effective. You can learn more about this important topic in Retention - Closing the Back Door.
The second is "basic" Predictive Modeling. It searches for profitable customers buried in larger segments that are generally marginal or unprofitable. These are "nuggets of gold" that you can take straight to your bank! You can learn more about Predictive Modeling in Prediction - Finding Gold in the Data.
The second is an extension of basic predictive modeling which MAG has invented. It is called ABT Analysis. It uses predictive modeling techniques to describe three kinds of customers: Advocates, Buyers and Tryers. Then it goes on to reveal how to "grow" Buyers into Advocates and how to find Buyers among the Tryers. ABT Analysis combines the best of descriptive modeling techniques with the power of predictive modeling. The result is greater than the sum of the parts. You can learn more about ABT Analysis in ABT Analysis - The Supermodel!
WHAT'S THE BOTTOM LINE?
Each one of these different data-driven tools will help improve sales, profits and marketing decisions. However, they vary widely in complexity and cost. The early steps are relatively simple, easy, fast and inexpensive. Later steps are none of these. So, experienced judgement is required to know when "taking the next step" isn't worth the effort and cost.
Engaging a consultant with broad and proven experience will help you make the best use of these data-driven tools. This is true whether you outsource the analysis work or do most of it in-house with your own resources. You can learn more about how to find and work with an expert marketing consultant in Consultants - Getting Your Money's Worth.
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