Analytics Is Getting Better
At Predicting Individual
Consumer Behavior And
Prescribing Business Actions
Written by Rob Lane.
Every time we electronically interact with a brand or its website, we leave behind a trail of data touch points. Being the creatures of habit that we are, hidden in this digital trail are insights about our buying patterns, our personal preferences for new things, and even hints as to what we are likely to buy next. Deciphering this data into information retailers can understand and act on is at the heart of analytics for ecommerce.
Many companies are utilizing various levels of descriptive analytics to learn what has happened to them in the past, and predictive analytics to predict future consumer patterns. Prescriptive analytics goes a step further, learning from consumer behaviour in order to prescribe actions designed to attract and retain customers at various points in the consumer lifecycle.
Analytics has come a long way from its humble beginnings in the late 90s, when it first appeared as a way to quickly process mundane website tracking results.
2. Google Analytics Arrives
In 1997, Paul Muret invented the first version of Google Analytics, called Urchin, as a service add-on for his web development company:
“One of our large clients was struggling with the fact that it took 24 hours to process a single day’s worth of website tracking results. We tried out our new analytics tool, and it took 15 minutes to process the same data. That’s when the light bulb went off – that Urchin was for real.”
Google Analytics has since gone on to be the global leader in website traffic analysis, paving the way for a new crop of ecommerce analytics platforms that provide digestible bits of data, in the form of descriptive analytics, to retailers about their customers’ digital interactions.
3. Descriptive Analytics:
What Happened To Us?
According to Dr. Michael Wu, Chief scientist of Lithium Technologies in San Francisco:
“Over 80% of the business analytics, especially social analytics are descriptive analytics. They compute descriptive statistics… [such as] mentions, fans, followers, page views, kudos, +1s, check-ins, pins, etc. There are literally thousands of these metrics.”
Descriptive statistics can provide valuable insights for business decision makers, but are limited in that they only aggregate, summarize and display historical data. Even what many consider to be advanced analytics are really just applications of filters on the data, such as geo-location, before producing advanced descriptive analytics.
4. Predictive Analytics:
What Are My Customer Going To Do?
Now days, business decision makers want to know where things are heading, not just where they’ve been:
“Predictive analytics uses many techniques from data mining, statistics, modelling, machine learning, and artificial intelligence to analyze current data to make predictions about the future.”
By segmenting their market into distinguishable groups, based on a prediction of future buying patterns and preferences, companies can more effectively pitch products, offers and messages that are more likely to resonate with specific consumer segments:
“Utilizing predictive analytics, previously hidden patterns in the data help organizations generate more in-depth customer segments. The resulting segmentation is more precise and nuanced, and is ultimately based on the likelihood that a consumer will accept a given offer.”
Predictive analytics is light years ahead of simple data aggregation and output, and can provide valuable insights as to what managers should do in order to appeal to certain segments; however, it still leaves lots of room for errors based on assumptions from the past, and the limits of lumping people into groups. According to the distinguished author of Big Data at Work and Competing on Analytics, Professor Thomas H. Davenport:
“The big assumption in predictive analytics is that the future will continue to be like the past…The great—and scary—example here is the financial crisis of 2008-9, caused largely by invalid models predicting how likely mortgage customers were to repay their loans.”
5. Prescriptive Analytics:
What Are We Going To Do?
The next level of analytics technology for business, prescriptive analytics, involves combining what’s been learned in the past, taking into account present consumer activities in real-time, and suggesting a course of action for business managers:
“Prescriptive analytics is related to both descriptive and predictive analytics. While descriptive analytics aims to provide insight into what has happened and predictive analytics helps model and forecast what might happen, prescriptive analytics seeks to determine the best solution or outcome among various choices, given the known parameters”
When the marketing actions suggested by prescriptive analytics are effectively automated, companies save on the time and labour involved in execution, which can be significant especially for small and medium sized businesses. When the output to consumers is customized at a 1-to-1 level, recognizing each customer as an individual, retention rates can soar, paving the way for real business growth.
6. What’s Next?
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