Having been involved in more than 100 Customer Intelligence sales and implementation cycles, here are six factors that determine customer intelligence.

6 Factors That Determine Customer Intelligence

The first thing I always emphasize is determining what success looks like. Simply installing software that functions, but never gets user adoption is not success. Having software that users love that does not improve business process is not success. Software that costs more to maintain then it generates in incremental revenue is not success.

Success is functioning software that is adopted by users and enhances business processes while increasing revenue and minimizing costs.

That’s a lot. It’s no wonder that so many CI projects don’t achieve that level of success.

Here are the six factors that must be present for your CI project to succeed.

1. Where is the data?

Most organizations start CI projects trying to define a data structure that will be used in the marketing process as if once that definition is set, everything will work perfectly from there. That’s never going to happen.

The truth is that data needed for marketing is always fluid in an organization. There is:

  • Customer data
  • Transaction data
  • Web data
  • Real-time data
  • Analytics data
  • Inventory data
  • Financial data

And in the emerging connected world sensor data, speech data, search data, cloud data, and on and on and on. Some of this data is structured, some of it is unstructured. Some is of it is big. Some is relational. Some is still in Microsoft Excel.

Successful projects start with manageable pieces of this massive data puzzle. Choose the data you need to take targeted action immediately. You must be able to identify key records that you want to target. If you are going to market to individuals, determine how individuals are defined in your data. If it is an email address, find out how the email address is stored. Yes, you will probably want to market at different levels. That’s okay.

The biggest mistake you can make is thinking that all the data questions that have to be answered before the project starts. Your marketing data sources will never be complete. The structure and the content will always be shifting. The core will remain stable, but there will always be changes.

Three years ago, no one was wondering how streaming GPS data was going to be used in a marketing database. When you start you CI project, keep in mind that there will always be changing data requirements. Look to solutions that allow for those type of changes.

2. Process

Technology is great, and it should be used to enable processes. But when processes are so inflexible that technology has to be customized, or key capabilities are not used, or the technology is flat out ignored, just to accommodate those processes, success is difficult to achieve.

The first step in implementing a CI solution is to examine your processes. Which ones can change? Which ones cannot? And when you find those that cannot change, an objective assessment needs to be made as to why those processes cannot change.

So many good solutions have been crippled because of tribal tradition. Or habit. Or the way it has always been done. Or (one of my favorites) the process was put into place to accommodate (insert your favorite obsolete technology), which was abandoned years ago.

Bottom line here is if you are going to install a modern software system, all your processes are subject to change. Your system, and your success, will be limited by your most inflexible process.

To achieve success, you need to build successful processes. You then are using software to enable a process. Change is hard. Find an external change agent to be your advocate when beloved processes need to change.

3. The right people

When you start down the road of improving your customer intelligence systems, you do so because there is a consensus that improvements can be made. Let’s be frank, a lot of things might just not be working right, or you just cannot do what you want with what you have.

That is most often not a people problem. We have already talked about data and process. But what about your people? They are probably overworked and stressed. They are going to need help getting new systems online. They are going to have to map old processes, figure out where the data lives, learn new tools, and countless other tasks on the project plan. Oh, and they will also need to do their regular jobs. On time. Without mistakes.

I cannot tell you how many project plans I have seen where there are several, key resources, with 80 percent of their week dedicated to the project and another 80 percent (you can do the math) of their week dedicated to business as usual. These people are clearly the right people for the job, but they are in an impossible situation.

To be successful, organizations must provide their people with the time and authority to succeed. The most effective way to do this is to partner with experts. Experts in the tools being installed. Experts in data design. Experts in the industry. Demand, and be willing to pay for, great implementations. Too many times, I have seen projects that settle for inexperienced implementers to save some money. Don’t fall into that trap. The costs of a failed implementation far outweigh the high price of true experts.

Related: Big Data: Why It’s Important and How It’s Changing the Industry? 

4. Executive commitment

If you are a member of the executive team, this section is for you. If you are trying to get commitment from upper management, this is what they need to know. Since you could use this in an executive presentation, I’ll do it this way:

There will be bumps in the road. Schedules will slip. Scope will change. No can anticipate everything.

Integrate with old, existing systems only when necessary, not just because is “in scope” or “out of scope.”

Balance that with clear definitions of what is “in scope” and what is “out of scope.” The areas that your people are trying to figure out are your grey areas. Are they necessary?

If the process is unnecessary, it is time to end that process.

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