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Proactive BI vs Predictive Analytics

Published on 15 May 15
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It's hard not to notice these days that predictive analytics is quite rapidly being adopted by the business world in a wide range of industries. It is being used in a number of functional areas, and especially in the marketing and customer relationship management domain for purposes such as identification of new potential customers, and for improved identification of cross-selling and up-selling opportunities.
The usage of true predictive analytics involves the development of statistical or probabilistic models that can be used to to find causative or predictive relationships based on selected variables. The development and refinement of these models is based on the availability of adequate amounts of historic data. The better the quality of data, and the more of it that is available, the better the quality (and hence usability) of the model that is developed. From concept to production, the development of these models is an exercise in patience and perseverance, along with creative thinking from the business angle to ask the right questions.
It cannot be restated often enough, though, that asking the right questions is really the key to the beginning of the whole exercise of using analytics of any sort productively. By "productively", of course, one means that the usage of analytics results in a positive return of some sort for the business. There is immense value to potentially be found in bringing together various types of skills, expertise and experience to embark on predictive analytics projects, but while thought and hard resources are invested in that doing that, it may be well worth not losing sight of the power of traditional BI.
As in the case of predictive analytics, perhaps the most important starting point is to ask the right business questions. Answering these questions doesn't necessarily need complex mathematical models, new languages and tools, large volumes of data, or the use of forecasts. Quite often, existing transaction data captured and stored for various traditional purposes in structured databases can reveal valuable insights by proactively using simple queries to answer not only "what happened?", but also "what didn't happen?".
For example, there's a lot of focus and effort that goes into the identification and acquisition of new customers. There's also a lot of focus and effort that goes into servicing and supporting transacting customers through the use of help desk, self-help websites, data services, and so on. But how many companies go back to find out why newly acquired customers never came back again or didn't transact in the first place? Perhaps there's value in understanding why they didn't proceed with a second purchase, why they switched loyalties after just one (or maybe even no) experience.
Historic transactions relating to customer registration and purchases can be used to find out not just what those customers have been buying, but also who stopped buying over time, or who paused to look but did not buy, or even who signed up but didn't consume. This information can be quite likely be found with traditional business intelligence infrastructure, and it should be done proactively and regularly. I was surprised recently, when an ISP that I'd signed up with messaged me to ask why I hadn't used their service for a few weeks even though I was still a subscriber. I liked that they'd noticed. That experience made me think about other industry sectors that have very competitive players who take great pains to send customers reminders about due dates, upcoming buying opportunities, etc. And sure, many also take pains to collect feedback after each purchase, but come to think of it, not that many ask customers that haven't shown up again for a long time why they haven't done so. It doesn't need too much complexity to find out the answer.
The bottom line here is that analytics of any kind can indeed be very useful, but before one thinks about whether they're using the latest and greatest tools, technologies and methodologies that the rest of the world seems to be getting into, it's asking the right business question first that is a key factor leading to the revelation of the most rewarding business insights.
It's hard not to notice these days that predictive analytics is quite rapidly being adopted by the business world in a wide range of industries. It is being used in a number of functional areas, and especially in the marketing and customer relationship management domain for purposes such as identification of new potential customers, and for improved identification of cross-selling and up-selling opportunities.

The usage of true predictive analytics involves the development of statistical or probabilistic models that can be used to to find causative or predictive relationships based on selected variables. The development and refinement of these models is based on the availability of adequate amounts of historic data. The better the quality of data, and the more of it that is available, the better the quality (and hence usability) of the model that is developed. From concept to production, the development of these models is an exercise in patience and perseverance, along with creative thinking from the business angle to ask the right questions.

It cannot be restated often enough, though, that asking the right questions is really the key to the beginning of the whole exercise of using analytics of any sort productively. By "productively", of course, one means that the usage of analytics results in a positive return of some sort for the business. There is immense value to potentially be found in bringing together various types of skills, expertise and experience to embark on predictive analytics projects, but while thought and hard resources are invested in that doing that, it may be well worth not losing sight of the power of traditional BI.

As in the case of predictive analytics, perhaps the most important starting point is to ask the right business questions. Answering these questions doesn't necessarily need complex mathematical models, new languages and tools, large volumes of data, or the use of forecasts. Quite often, existing transaction data captured and stored for various traditional purposes in structured databases can reveal valuable insights by proactively using simple queries to answer not only "what happened?", but also "what didn't happen?".

For example, there's a lot of focus and effort that goes into the identification and acquisition of new customers. There's also a lot of focus and effort that goes into servicing and supporting transacting customers through the use of help desk, self-help websites, data services, and so on. But how many companies go back to find out why newly acquired customers never came back again or didn't transact in the first place? Perhaps there's value in understanding why they didn't proceed with a second purchase, why they switched loyalties after just one (or maybe even no) experience.

Historic transactions relating to customer registration and purchases can be used to find out not just what those customers have been buying, but also who stopped buying over time, or who paused to look but did not buy, or even who signed up but didn't consume. This information can be quite likely be found with traditional business intelligence infrastructure, and it should be done proactively and regularly. I was surprised recently, when an ISP that I'd signed up with messaged me to ask why I hadn't used their service for a few weeks even though I was still a subscriber. I liked that they'd noticed. That experience made me think about other industry sectors that have very competitive players who take great pains to send customers reminders about due dates, upcoming buying opportunities, etc. And sure, many also take pains to collect feedback after each purchase, but come to think of it, not that many ask customers that haven't shown up again for a long time why they haven't done so. It doesn't need too much complexity to find out the answer.

The bottom line here is that analytics of any kind can indeed be very useful, but before one thinks about whether they're using the latest and greatest tools, technologies and methodologies that the rest of the world seems to be getting into, it's asking the right business question first that is a key factor leading to the revelation of the most rewarding business insights.

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