Enterprise Decision Management - a Weblog

Ajay Kelkar had a comment on - Live from Teradata (almost) - Improving Customer Experience with EDM. Ajay made the point that a business entering "take off" stage is particularly interesting as there is a belief that you should get to market fast and then, as market changes keep hitting you ,make changes. This gets to a couple of key EDM points - the need to focus not just on automating decisions but on improving them and on adaptive control. Using enterprise decision management, EDM, in a take-off phase is pretty straightforward and high-value:

1. Use business rules to build in agility.
   Focus on the key operational decisions (micro-decisions) that drive your systems and processes. Make sure these are automated as decision services using business rules (FAQ)as this will make it easy to evolve and change your decision-making as circumstances change. Not only does this help you get started quicker, it also helps you respond rapidly to competitors as well as your own changing customer base and business needed.
2. Rules can be judgmental to start with, or even guesses
   Don't be paralyzed by the need to find the "right" rules. These may not exist yet for your business and, even if they do, you may not be able to find them and implement them before they cease to be "right". Get started and focus on learning fast.
3. Use performance management and business intelligent to see what works
   Standard performance management dashboards and BI reports can be used to give you asense of how things are going. Because your core decisions are implemented in an easy to changeway using business rules, the same business users who see the reports can tinkering with and improve the rules that drive your business (though there are some secrets to setting this up).
4. Establish anadaptive control approach
   Adaptive control gives you a more systematic way to try different approaches and drive towards ever-improving results. It is worth implementing for your critical decisions and will require both an understanding of chamption/challenger testing and some infrastructure.
5. Gradually add analytics as you become more established
   As you grow andhave more data as well as more experience, you can and should replace your judgmental rules with analytically derived ones. Additionally you can add predictive analytics (FAQ) based on the experience you gain with customers and prospects and use these predictions to improve your decision making and perhaps even extend it in new ways.

Of course you should also buy and read Smart (Enough) Systems and subscribe to this blog!

Technorati Tags: agility, analytics, business agility, business rules, decision service, EDM, enterprise decision management, predictive analytics, Smart (Enough) Systems, smartenoughsystems

Mike Schaffner had a nice post that I saw this morning - Some Technology Suggestions for Airline Customer Service. He pointed out that a couple of simple things would represent much better customer service when one is flying. His examples were providing connecting flight information to passengers in flight, especially when flights are disrupted, and making sure that people whose baggage missed the flight don't waste their time waiting for it. This got me thinking about extending this to other aspects of customer service and I realized that the way to approach this would be to consider the decisions that airline passengers must make and then think how you could help. Here goes, with thoughts as to how a passenger might make those decisions and what might help.

Before check in

• Should I check a bag or try for carry-on?
  Depending on the plane used and the degree to which a flight is full, the risk of having to check a bad anyway varies. Bag check-in times vary by airport and time of day and the risk of a checked bag failing to make a connection depends on the time available and airport at which the transfer happens.
• When should I leave for the airport?
  The likely departure time of the flight, the decision about checking bags, the security line and bag check in wait at the airport given the time of day, the passenger's tolerance for risk, how they are getting to the airport and the time it typically takes to park and get to the terminal if they are driving all contribute.

At check in

• Should I buy an upgrade?
  The likelihood of a free upgrade from frequent flier status and the availability of bulkhead/exit row seats might all play a role.
• What seat should I pick?
  Given the layout of the plane and the nature of available seats (see this site for instance) as well as the passenger's preference this might be a non trivial decision.
• When should I get to the gate?
  A function of upgrade status, boarding position, need to find space for carry on and likely actual departure time.

At the gate

• Should I board the plane?
  If the plane is delayed or a connection has been canceled or delayed, perhaps the trip is no longer worthwhile or a complete re-routing is called for.

On arrival

• Where should I go next?
  Where is my connecting flight/baggage?
• Can I make my connecting flight and doI need to hurry?
  When it is going to leave, how long does it take to get there from here, will it wait for me?
• What are my options if I miss my connecting flight?
  How can I complete my travel if I miss the connection

In every case the airline has much of, if not all, the information needed to advise the passenger of the "best" decision. A regular flier who might be willing to share some additional information (like how they travel to the airport or where they are going to leave from for a given trip) could improve the quality of decision-making. In most cases there are rules from the airline's experience, rules from third parties (like security requirements), data from various sources and customer preference rules (that could be defaulted or inferred from other customers). Some predictive analytics also come into play, like predicting how full a flight is likely to be (from past history) or the likelihood of a weather delay. A focus on these decisions, and the automation and ongoing management and improvement of them, could deliver a much higher level of customer service. With more screens in airports that are truly programmable and with the increasing percentage of passengers with SMS or email enabled phones, there seems little reason why airlines could not focus on these customer service decision-points and improve the visibility and decision-making accuracy of their passengers to everyone's benefit.

Technorati Tags: analytics, business rules, customer experience, Customer Service, customer-centric, personalization, predictive analytics, airline

A marketing focus today. More particularly a focus on marketing to existing customers prompted by three distinct posts. Firstly there was Growing Business the Old-FashionedWayover on Customers Rock discussing how concentrating on existing customers can be very profitable. The post suggested, among other things that you think about the last 5 communications you had with an existing customer and thought about how those interactions will make those existing customers feel. As one of the posts referenced said, personalize your interactions so that your customers feel like you are talking to them specifically. This requires a focus on micro not macrodecisions- a micro decision would be the content of this letter going to this customer (1:1) where a macro decision would be "what kind of letter should we send existing customers" (1:Many). You should also not just remember your customer's history but use it - leverage all thatCRM information. Your best next action (discussed in this InterACT sessionWhat's Next? The "Best Next Action")could be a word of thanks, for instance, not an offer. Lastly, existing customers should get special promotions too, which brings me to the second post- Loyalty Programs: The What and the Whyover on EbizVitals.

This post revisited the old "corner store" idea (blogged about here in Customer Loyalty, EDM and "the corner store") and gave a good general overview of what a loyalty program is but raised an interesting question - does your loyalty program simply reward the customer activities you would have got anyway or does it actually drive new ones? For instance, in retail, do your customers shop with you because of the loyalty program or simply because your store is nearer? If the loyalty program never overcomes a customer's tendency to go to the nearest store, what good is it? Well first, regardless, you can capture a great deal of interesting data about your customers and how they shop. This data can be put to work to improve marketing, store-layout and many other decisions as I have discussed in this post on using EDM in the loyalty economyand as evidenced by the EDM-Driven MyCokeRewards site. You can also apply more sophisticated analytics to see what rewards or rebates matter to a customer and might therefore actually change their behavior. A good example of this would be an offer that required a slight increase in spend or frequency over that particular customer's norm to get a special bonus.

In a world of hits and nichesand multi-channel marketing, one of the questions is where to beginand this was the topic of the third marketing post I saw, over on the Unica blog. Two particular quotes leaped out at me:

"In order to embrace multichannel marketing, don't wait until the day that you can put the big 360 degree CRM data warehouse into place"
"turn the insights into intelligent marketing programs"

Absolutely! Start improving decisions with the data you have or can get and focus on adding the information that will help you do better over time. The post had a great example of a simple retention risk decision (using just web analytics to find customers whose visits declined) that could be gradually made smarter for instance by integrating offline sales data to eliminate customers who had transitioned from web to store from your list of "at risk" customers. When usingEDM to focus on a customer centric approach, a good way to start is by adopting a rules-only decision that can gradually be enhanced by more and more analytics over time. The critical thing is to identify the decisions that matter and then work from there back into the various channels that need the decision and the various data sources that might improve it.

Technorati Tags: 1:1, best next action, CBG, coke, CRM, cross-channel, customer data, customer decisions, customer experience, Customer Service, customer-centric, EDM, enterprise decision management, loyalty, marketing, multi-channel, mycoke, personalization, precision marketing, the long tail

Scott Thurm recently wrote a piece in the WSJ Marketplace section - Now, It's Business By Data, but Numbers Still Can't Tell Future. He talked about the growing trend of trying to run companies more analytically, more "by the numbers", and the success some of those companies have had with this approach including those profiled in Tom Davenport's book "Competing on Analytics" (reviewed here). He quoted Robert Sutton (one of the authors of Hard Facts, Dangerous Half-Truths And Total Nonsense, reviewed here) who contrasted running a business by the numbers with running it based on "faith, fear, superstition and mindless imitation"! However, Scott then goes on to identify two key risks inherent in an analytic or data-driven approach.

• Change upsets the basis for the analysis
• Too much focus on analysis stifles the creativity needed for innovation and tomorrow's growth

These are both good points and made me think about enterprise decision management(EDM) in this context. Now Tom Davenporttalked about the need to make"quick, accurate decisions on an industrialized scale" when he reviewed Smart (Enough) Systems. These kinds of high-volume, operational decisions are the focus for EDMand change to the environment in which you are making those decisions must be considered. No decision can be automated in a way that will ensure it remains effective indefinitely - changes to competitors, markets, products and economic conditions will conspire to ensure it degrades over time. If you are lucky, it might degrade gently. If you are unlucky, some sudden shift could ruin you.

This need to manage and improve decisions in the face of change is why challengers is so important to the successful adoption of decision automation. With adaptive control you constantly test your current decision making approach against challengers to see if any of them work better. This helps both find better approaches and spot when your existing approach is no longer optimal. An infrastructure for adaptive control also allows you to move into true experimental design where you are systematically checking a large number of potential approaches and aiming for continuous optimization.

Some changes are too sudden for this approach, however, as the results of many decisions take a finite length of time to collect. A very rapid change might mean you are in trouble before the results show it. Using decision simulation techniques, and a robust model of what influences the decision, many organizations are now running scenarios (such as much higher interest rates or a bad hurricane season) to come up with the rules and analytic models that work best in those circumstances. These decision approaches can be kept on the shelf ready to go in case one of those major changes should occur. Even this does not completely solve the need to respond to unexpected change, for which a general focus on agile information systems is about all you can do, but it limits the circumstances in which you will have no response beyond "gut feel". Now that I have finished Harry Potter 7 (of which more later), I am also in the middle of reading Nassim Taleb'sThe Black Swan: The Impact of the Highly Improbableand I will write up a review and some thoughts on managing "black swan" events sometime soon.

Even with adaptive control you run the risk that you are perfecting today's business rather than thinking creatively about tomorrow's possibilities. However, when you are dealing with massive scale (millions of accounts, hundreds of thousands of customers etc), you really have to be able to model the impact of your creativity before you put it into production. Changing a web page layout may be easy enough to try and see if it improves people's user interaction, but introducing new pricing or a new product without having some idea what impact if might have on other products, shelf space, marketing campaigns etc is probably foolish. I would also suggest that creativity and analytics can go hand in hand, as I have discussed before in my review of Malcolm Gladwell's Blinkand ofLarry Rosenberger's "Future of Analytics" presentation at InterACT last year.

Technorati Tags: adaptive control, agility, analytic application, analytics, automated decision making, competing on analytics, decision management, EDM, enterprise decision management, evidence-based, micro decisions, optimization, predictive analytics, Smart (Enough) Systems, smartenoughsystems, Tom Davenport

Craig had a good post today - Obtaining Management Support for Data Mining and BI. I liked the post because he focused first on the business objective - the decision you are trying to improve - and only then on the mechanics of data assembly, cleansing etc. His steps would work just as well when thinking about how to get support for decision management projects also and when thinking about how to apply data mining and predictive analytics to decision automation and management, some posts I wrote on first aspects ofreadiness on my other blog seem apropos:

• Business and IT collaboration
• Data
• Analytic understanding
• Organizational change
• Operational focus

I also have a whole section on data mining.

Technorati Tags: analytic application, analytics, data mining, decision management, EDM, enterprise decision management, predictive analytics

One of the reasons for a focus on decisions and decision services is to complete the decomposition of the old, monolithic application (thanks Mark). Think about it. If you embed decisions - business logic - in your applications, you hide these decisions from view and risk those decisions becoming a liability when the world changes and they do not. Most application development techniques hide decisions inside applications. This makes development, and even more maintenance, very expensive.You might also delegate details toprogrammers when you want to have those programmers collaborate with business users on the required logic. Applying EDM you can empower business users to manage those decisions, which reduces the time to make changes and reduces maintenance expenses.

A colleague of mine (thanks Mark) pointed out that such as approach is the last step in the decomposition of traditional applications. Not so long ago, applications (especially enterprise applications) were monolithic, containing data, user interfaces, business logic, and process flow in one block of code. We then begun to decompose these applications - managing data in a database that ran across applications, using portals and other clients to manage the user interface across applications and, most recently, BPMSs to externalize workflow and build cross-application flows effectively. One more step is required - separating out the decisions and managing them so they too can be shared across applications. This could be the most important advance to date.

Technorati Tags: business process, business rules, decision management, decision service, enterprise application, predictive analytics, legacy modernization

I have been thinking about Marketing Operations (nicely described by Gary Katz here) and I saw a post on Why is marketing operations so important?on Unica's blog.It contained the great phrase:

"What are the reasons for the growing workload? The most common cause we witness is related to the proliferation of addressable media, and increasingly granular segmentation being used to target audiences with these media"

This is the nub of the problem - more complexity in decision making. Then I saw this post on a 350 degree view of the customer(yes, 350) which focused on a parallel and reinforcing problem in marketing operations:

"But now that creating the 350 degree view is largely a solved problem, providing access becomes the limiting factor to organizations fully harnessing customer data to drive customer-centric marketing"

So it's more complex to make decisions and you have more and more data that your front line staff understand less and less well. Sounds like a job for enterprise decision management (EDM)! By focusing on the operational decisions in marketing (what offer to make to this customer at this time), automating them and using analytics to improve them, you can make a real difference to how your marketing operations function. Front-line staff get decision (action) recommendations not just more data, all your channels deliver a consistent answer, and your marketing strategy is reflected directly in your marketing operations. While there are many issues to be considered beyond the technology ones (Gary had another good post on this Marketing Operations: Beyond the Bits and Bytes), EDM is a great approach to apply to get control or your marketing operations.

On this topic I recently posted about how Yahoo's senior marketer tells you to use EDM (kinda)thanks to channel proliferation and real-time data, how EDMcan deliver oncustomer centricityand on Data Mining, Predictive Analytics and Markets of 1. I should also point out that Fair Isaac partners with Unica and that I have blogged about a nice Unica White Paperand a presentationgiven by a colleague to a Unica conference.

Technorati Tags: 1:1, CRM, cross-channel, customer decision engine, customer decisions, customer-centric, customization, decision-centric, EDM, enterprise decision management, extreme personalization, loyalty, marketing, multi-channel, personalization, retail, the long tail

Scott Sehlhorst, with whom I am presenting at Business Rules Forum this year, just posted Use Case Example With Business Rules. In the post Scott identifies 4 opportunities, in an ATM withdrawal use case, to find decisions and I thought I would expand on it (as part of our ongoing efforts to develop some coherent thoughts and guidance around rules and requirements). Below are the opportunities I see (added one in bold) with my thoughts on the decisions contained:

• Validates the card information.
• Is the card valid?
  This might be simple (matching account numbers) or complex (matching account number, internal records and other coded information on the card). It might also be different at an ATM with a thumbprint reader or iris scanner!
• Is the card compromised?
  This might simply involve checking a list of reported stolen cards but it might also involve more complex event processing kind of checks (has this card been used at another ATM so recently that we suspect there are multiple copies, what risk score comes back from a neural net designed to detect fraud).
• Who is the customer?
  Simple decision so we know who we are dealing with (which will become important further on)
• Selects transaction.
• What options should we display?
  Although Scott does not think this is an explicit decision, I do. Scott notes that the options available may change over time but there are other issues. After all we know who you are at this point. As a result we should be able to personalize this and:
• See if there is a transaction you do so often that the right thing to do is display that option only (to make it quick) with an option to do "something else"
• See if there is a small set of transactions you favor over others for a similar reason or if predictive model suggests that some options are much more likely to be relevant than others
• Identify the options that are reasonable (if you only have one account then don't display the option to transfer money for instance
• Validates transaction details.
• Is the transaction valid?
  Could be simple (enough cash in the account) or more complex (is this kind of transfer allowed)
• Canthe ATMcomplete the transaction?
  Could be simple (enough cash in the ATM) or more complex (system availability for a linked system)
• Make Offer.
• Is a follow-on offer appropriate
  Once the transaction is done the ATM could choose to make a follow-on offer. Deciding on this might be a function of the time since the last customer (a measure of busyness that might cause us to prioritize the next customer over a follow-on activitiy), the customer's own preferences, prior history with this customer, best next action for this customer and so on.
• Updates the account.
• What fee is there?
  Might be a simple calculation or a very complex one in terms of good customers getting fees waived etc.
• Does the transaction require follow-up?
  "Know your customer" legal requirements might be involved, though these are likely to be handled in the back office not at the ATM

This kind of more sophisticated interaction is part of what I call building the bank of the futureand involves a ruthless focus on micro decisions. BTW I would also recommend another book,Use Cases: Requirements in Context(reviewed here), as it not only has good advice on use case development, it also focuses on keeping rules separate.

Technorati Tags: analytics, best next action, business rules, customer decisions, customer-centric, decision management, event processing, fraud, know your customer, marketing, personalization, predictive analytics, requirements, Scott Sehlhorst, use case

I saw this post over on the Web Gambit today - How Agile is your Architecture?- where Karthik emphasizes the Agile Manifesto's Responding to change over following a plan. He is talking about architecture agility and says

"After a certain point, the software's architecture becomes rigid and inflexible, and only allows for minute changes to its supported feature set"

While his focus is on technology/platform change, it seems to me that enterprise decision management, with its focus on decisions and Decision Services, offers the opportunity to add a layer of very agile services to your architecture. Not only is this a recognition of the change over plan reality of most projects (it ensures that the core business logic of the application can be developed for bothflexibility and rigor), it might also be considered an effective way towrite maintainable codein an era in which retainingknowledgeable staff is increasingly difficult. One could consider this an approach to "lean" application development and maintenancetoo. Decisionmanagement and technology can drive agility into your architecture by delivering a layer that is easy to change as business needs change - a decision layer- especially a layer that can be deployed to multiple platforms (Java, .NET etc).

Technorati Tags: agile, agility, business agility, business rules, change time, decision service, enterprise decision management, decision management

Two articles made me think about the application of enterprise decision management, EDM, in the world of customer experience. First, I saw the one Jeff Kaplan wrote - "Data Mining as a Service: The Prediction is Not in the Box". Jeff asked the relevant question very early on:

"Why were there so many failed enterprise customer relationship management (CRM) implementations? "

and he went on to suggest that the use of predictive analytics (see my FAQ) should be pat of the solution. He then assets that:

"The key challenge that packaged predictive analytics software has not been able to crack is how to extract knowledge from data quickly and put it into the hands of marketers to make better, more informed decisions"

Here I have to disagree with him slightly - it is just not about putting knowledge in the hands of marketers so they can make better decisions, it is about putting "knowledge" into those failed CRM implementations so thatthe systemscan make better decisions. This requires more than just a focus on predictive analytics - it requires a focus on automating, managing and improving decisions or EDM. Jeff's outline for applying predictive analytics is a good one, however, in particular as he correctly identifies need to focus on the problem first and then gather and analyze the data (as distinct from the approach often taken of collecting data and hoping something can be discovered from it). I would add a couple of things to his outline:

• You need to focus on the decisions you want to improve - cross-sell offer, home page design, call routing or whatever - as this gives you the problem statement you need
• You need business rules not just analytics. There are rules derived from policy, from regulations, from experience or even managed directly by customersthat must be considered. A decision is a great place to gring the rules and analytics you need together.
• When you get into the automation of decisions you need adaptive controlto manage your experimentation and testing and to ensure that you can act on the results you get. This boils down to a software architecture to let you do in your system what your analysts would do in their heads or with Excel - compare approaches, analyze them for success, continually improve.

You should read the article as it makes some great points about predictive analytics and the challenges in using them, especially for the first time. Moving on, the second article was by Rob Walker of Chordiant - Next-Best-Action Marketing: Creating the Segment of One. My favorite quote from this one was:

"combine predictive models that were once the province of statisticians with a completely new breed of user-oriented business rules that can significantly improve the customer interaction experience"

Wow - sounds like EDM to me :-) Actually my friends at Chordiant call it "Chordiant Decision Management" and are focused more singularly on customer experience decisions but hey, close enough. I have already blogged this week about using EDM to meet CRM challenges and use what you know about your customersand interestingly enough Analytics CRM = Happinesswas one of my first posts on the blog - clearly this is a topic that comes up a lot. Rob's article was very informative, and well worth a read, as he (like me) is focused on a "corporate decisioning hub" for delivering decisions hither and yon (check out this article for my POV as written up in The business rules revolution). Building a customer intent driven organizationstakes this kind of ruthless focus on customer treatment decisions as being customer-centric means being decision-centric. Such a focus allows you to deliver extreme personalization and markets of 1and 1:1 communicationwith scale. Using analytics tosegmentcustomers and rules and more analytics to target them very precisely helps increase customer loyalty and perhaps recreate the feel of "the corner store". It also allows you to survive in a Long Tail world with both hits and niches. I have written before about both EDM and Customer Centricityand The "Best Next Action"as well as about the experience of a Fair Isaac customer - Customer Centricity in Action: Best Buy.

If you don't want to read all those blog postings, you could buy and read Smart (Enough) Systems - the book I wrote with Neil Raden - as this is exactly the kind of situation that demands smarter (CRM) systems. Other books you might enjoy on this topic include Chocolates on the Pillow Aren't Enough(a great book on customer service), Competing on Analytics(a good introduction to the power of being an analytic competitor), Berry and Linoff's classic Data Mining Techniquesand (for those of you focused on lots of niches for your products). The Long Tail.

Technorati Tags: adaptive control, analytic application, analytics, best next action, business rules, competing on analytics, customer decision engine, customer decisions, customer experience, Customer Service, customer-centric, decision service, decision-centric, DM Review, EDM, enterprise decision management, extreme personalization, marketing, multi-channel, personalization, precision marketing, predictive analytics, Smart (Enough) Systems, smartenoughsystems, Chordiant

Scott Sehlhorst and I are presenting at Business Rules Forum this year on rules and requirements and thought we would use our blogs (his is here) to carry out a public discussion/presentation development process! He got it started with Business Rules And Requirements - Early Thoughtsand already has a couple of comments so I thought I would open it up here too.

I regularly blog about things like the other thing CIOs should know about requirementsand digging yourself out of the requirements tarpitin the requirements section of the blog. But now it's your turn (please). how do you manage rules and requirements? How do you help people determine rules separate from requirements or do you? What about processes - how do they fit? Any and all comments/thoughts welcomed. Scott and I are going to synthesize them and work an example between the blogs over the coming months so this should get interesting.

Technorati Tags: business agility, business rules, Business Rules Forum, requirements, Scott Sehlhorst, Tyner Blain

Two articles in CIO magazine caught my eye this week. Firstly, Meridith Levinson wrote Getting to Know Them, an article about some award-winning systems. This was a great article and a couple of things occurred to me as I read it in terms of how enterprise decision management or EDM could be applied.

• She emphasized that the key to better service for customers is improving the service offered by front line staff.
  My experience is that this often means empowering them to make decisions more effectively, the focus of EDM.
• There was a focus on customer-facing employees not technology
  This is true but customers use systems too. If a really good customer gets great service from an employee but rotten service from your website or IVR system, they will not feel the love. If you only focus on getting information to people then your systems will not meet the needs of your customers.
• Every one of the winners identified their business need first
  Often this means finding the decision you want to improve and then focusing on improving it. Not on collecting, cleaning or reporting data but on the decision you hope that data will improve.
• Continental Airlines example of rewarding delayed customers is a good example of an opportunity for EDM
  After all there are different possible rewards so you would like to be able to predict what might be appealing to a customer or segment and you need adaptive control to keep testing approaches to see what works.
• The example of automated re-booking is a great EDM example
  Take a stressful manual decision that must be completed in a short time window by someone who cannot have all the information at their figure tips and instead make an automated decision as to who is a really good customer (and who is not); then decide how much you are therefore willing to spend to give them a good experience; finally decide on the best possible rerouting you can find given those constraints. Decision-centric, rules and analytics. EDM.

• Ace's better targeting is another example
  You can get really personalized if you focus on the individual customer marketing decisions and combine both rules (as discussed in the article) with analytics (for segmentation and predicting customer behavior).

Alicia Acebo, Continental's data warehousing director, said"Before the data warehouse, the person who yelled the loudest got the best service. Now our most valuable customers get the best service," and that should be your objective. Just remember, to make that true, all the customer treatment decisions you make (through staff, through the website, through the options presented to a customer) should reflect what you know about your customer. To do this you should think ofyour home page as a decision and not a page so that you can generate a personalized page for every customer. You should focus on growth decisionsand deliver more (and better) self-service. This meansbecoming more customer-centric (which means being decision-centric).

The second article was one by Shawna McAlearney CRM: Challenges and Advice for CIOs in 2007. This seemed to be based in part on the work Accenture does with Montgomery Research (for whom I wrote a recent White Paper on Smart-Enough Customer Decisions). The article identified a number of pieces of advice for some of which I have some follow-up, EDM-centric thoughts.

• Striking a balance in how they use resources to market to the most valuable consumer segments;
  Analyze your data to find out what makes someone valuable, use predictive analytics to identify those who will be valuable and descriptive analytics to segment them. Apply this to all interactions, not just manual ones.
• Distinguishing themselves through customer interactions that support a branded customer experience;
  Again, every interaction contributes. Customers think that every decision you make about treating them is deliberate so you need to manage these decisions as though that were true.
• Using analytics tools to gain a deeper understanding of the actual intentions of customers in their own words.
  Understanding customers is not enough, you must be able to act on it and your systems must be able to act on it also.

EDM is a powerful tool for CRM and marketing. Use it.

Technorati Tags: adaptive control, analytics, best next action, business rules, competing on analytics, CRM, customer decisions, customer experience, Customer Service, customer-centric, decision-centric, EDM,