Intelligent Metrix

Data to Metrics to Insight to Intelligent Decisions

Why Business Intelligence is So Difficult

Reading the buzz on the Jim Davis’s presentation at SAS Global Executive Forum, what it made me realize is that if as an industry we can’t agree on what Business Intelligence is or Business Analytics, how are we supposed to make sense of it in implementation?

business intelligence confusionYou have analytics players, enterprise application vendors, business process consultants, and analysts all trying to sell the ‘hype’ of a better way to analyze your business and makes decisions.    SAS wants to sell their analytic solution that really pioneered data mining in businesses.  Oracle and IBM wants to push dashboard solutions that links to business processes and their enterprise applications.  Gartner that tries to tie together people, process, and technology but is really is focused on what technology to buy.  Then, you have consultants that are trying to help you implement the technology even as they document your processes.  The problem is that it’s all boiling down to the one with the best tool wins.

Enter in the ‘Business’ and now you have a problem.  All they want to know is how they can meet their business objectives.  IT is trying to sell the solution and make them understand the technology, and the business glazes over and can’t figure out what to focus on.  I’ve sat in these discussions where IT tells me, “You tell us what to do, we’ll do it.  Don’t worry about the solution.”  It is open ended.  This leads to IT unable to work towards tangible goals and results.  The business walks away frustrated, projects run from months into years, and original budgets are thrown out the window.  I liken these projects to Boston’s Big Dig.

Neil Raden provided a perfect way to get through the fluff and hype that surrounds analytics and business intelligence. See article From BI to Business Analytics, It’s All Fluff

“I don’t like the term business analytics; it doesn’t tell me anything. Frankly, I think business intelligence as a term is downright laughable, too. What does that mean? Is integrating data intelligence? Is generating reports intelligence? Maybe its informing, but isn’t intelligence something you HAVE not something you do? Does doing what we call BI lead to intelligence, or just some information? A long time ago we called this decision support, and that gets my vote.”

So here’s my take on what steps to take when and how to venture into BI and analytic solutions.

Steps:

  1. What decisions need to be made?
  2. At what point in our business and business processes are these decisions made?
  3. What information is needed at these points?
  4. How should our applications and data provide this information – triggers or visualization?

See the steps?  It starts with the business decion and ends in the technology.  So, when you begin to review vendors and solutions, make sure you have steps 1,2,3 in mind before you determine how to solve step 4.

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Translating Awareness to Consideration Set in B2B

Want to improve lead quality?  Focus on knowing when a customer includes you in their consideration set.marketing timing

It is one thing to get someone to notice you, it is quite another to get them to think of you when getting ready to make a purchase.  B2B marketing works to tie these aspects of a customer purchase cycle together through a strong call to action.  In the end, the holy grail when targeting the campaign is reaching the customers that are truly at the beginning of the purchase cycle.  The relevancy of a campaign isn’t just that you provide valuable content to someone that is the subject matter expert (SME) in their company, it is that it is relevant when the SME is ready to become engaged.

Right message, right person, RIGHT TIME.  Timing is everything.

Judging when a customer is ready to engage is not as allusive as you might think.  The key is to recognize behavioral aspects within you customer and contact base.  Opportunity segmentation has typically focused on financial transactions due to its availability and consistency.  It is effective when determining customer value and staying on top of purchase cycles.  Although, this fails to account for the “who” that acts with in high opportunity customers as key influencers and decision makers.  In addition, it fails to account for prospects you’ve brought in and engaged.

The other piece of opportunity identification through behavior analysis is recognizing how contacts are interacting with content on your website, responding to campaigns, support inquiries, and, if available, social media venues.  There are a several ways to leverage this type of information from the simple to more sophisticated predictive analysis.  It will depend on your level of ability to identify behavioral aspects of contacts and linking behavior information across various marketing venues.

  1. RFE Analysis (Recency, Frequency, Engagement) – A modified version of RFM (recency, frequency, monetary) which focused orders, replace M with E (Engagement), you can begin to identify behavioral aspects for simple segment selection.  E is the point when sales recognizes the opportunity and includes in a pipeline and confirmation that the customer includes you in the consideration set.  E can also be another type of event that the outcome is a face-to-face meeting, for example trade show attendence or in-person seminar.
  2. Reference/Word-of-Mouth – There are two aspects of this.  The first is that the contact will be a reference or unrequested acts on your behalf to influence others.  However, the other side is that they are actively seeking out other customer perspectives by reading other’s opinions and asking for opions.  Tying together campaign interactions with a transition to reference/word-of-mouth activity can provide insight that they are ready to engage.
  3. Predictive Analysis – The previous two approaches can be easily done through simple segmentation techniques.  Taking them a step further, you can apply predictive analytics to solidify benchmarks and KPIs.  Indexing of contacts’ behavior and mapping that to scorecards identifies pre-engagement contacts and customers.  The values can be dynamically set so that as contacts and customer reach thresholds they move into campaigns that are targeted to move them into sales engagements and support the sales engagement.

Are you tracking the transition from awareness to consideration?  What do you look at?

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