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Bringing the data to life – Four important lessons February 10, 2014

Posted by Ivor's Window to the IT and CRM World in Business Intelligence, CRM 2013, Management, Microsoft CRM, Microsoft CRM 2013, Microsoft Dynamics CRM, Training.

Bringing the data to life

We have a customer who has been using Microsoft Dynamics CRM for just over a year, they added ClickDimensions to the mix some months later and now have a real set of rich data with which to work.
The customer records are updated nightly through a process from a data warehouse and CRM is the mechanism for marketing and outbound communications.

Many companies talk about mining their data and have lofty ideas about what they want to do, these are often tabulated and postulated about before a new system is implemented where the reality of the data and the perceived marketing ideals do not really converge.
As I have found with my customer, learning about the data as the system evolves is the way that great marketing ideas come about and the requirements become more identifiable. I have watched this data come alive, I have seen how with filtering and intuitive understanding more and more effective marketing comes about.

The key lesson here is that during the analysis phase it should be generally understood that data mining and analysis are going to be important and the data model incorporated into CRM must be correctly designed, however presumptuous interpretations about what you perceive will be the results should be avoided. It is not a light switch and it is unlikely that on day one there will be this large data revelation.

Our customer is still learning, they understand that. They know that they don’t know everything there is to know about their data. ClickDimensions adds a whole new perspective to the data analytics and with Web capturing which we recently added, even more interesting data stories are beginning to unfold. There is no long list of things that are wanted, it is now much more spontaneous and with each iteration and Advanced Find query saved, more and more understanding is added to the overall model. It’s a building learning iterative process.

In effect they are bringing the data to life little by little, and when I am asked to assist and I see what is starting to happen, I can see the beginnings of the data taking on a life of its own. Their marketing model is all about customer retention and regular interaction which lends itself to this type of analytical systems approach.

Four important lessons
1. Good data is probably the first and most important element:
The core transactional data coming from the business systems via the data warehouse feed in this instance keeps the integrity of the data on a very sound footing. The understanding of the relationships between certain elements of the data within the customer record becomes pivotal to the analytics.
2. Constantly review your data and campaigns:
The customer found that the subsequent marketing data related to outbound campaigns and the resultant activities and further incoming data based on these campaigns all correlated together make for a full picture about how a specific customer was affected by an individual marketing campaign. When aggregated, segmented and analysed it showed trends that they did not perceive would be possible when starting out.
3. Make use of visual data:
They have also been using CRM dashboards to visualise the data, this too has been an evolving process and certainly were not defined up front. Dynamic exports of views and pivot tables to Excel can also be very effective.
4. Have the right people doing this work:
I also believe that the people doing this type of work, need to be marketers, not pure data analysts, they need an affinity for the business, an understanding of their industry coupled with the intuition to bring this all together. This just screams out to me as people who are passionate about their industry. Once they have a good system we can teach them how to get the most from their data within the system, and from that point forward it is growing and learning experience.
And the key to success is?
Start small, be pedantic about the quality of your data, record results and try and understand what they mean. Build on each step and I am sure in time you too will also start seeing your data coming alive.



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