Summary: Business Analytics represents a set of tools which has evolved from Data Warehousing and Business Intelligence to Big Data, with some proposing Analytics 3.0 as the next big thing. Understanding its evolution from the perspective of the customer centric web can help us see its real value and limitations, and where we should be investing.
How can we control our business better? How can we plan for it, particularly when it’s a large enterprise with many products and thousands of customers? This question is behind the evolution not just of Business Analytics but also of many advances in IT; we can summarize its development in the following table.
|Analytics 1.0.||Analytics 2.0.||Analytics 2.1.|
|Name||Data Warehousing, Business Intelligence.||Big Data||Information rich offerings.|
|What it does.||Gather and analyze operations data: point of sale, inventory, customer contact.||Vast data sets, much more powerful number crunching.||Use Big Data to improve operations and transactions.|
|Who uses it.||Most big companies.||A few big companies||Very few companies.|
|Weak points.||Stuck at reporting, seeing past trends, not actual analysis. Expensive and difficult to use for small and medium sized businesses.||An end in itself? Correlations, not causality.||Follows data, not actual customers.|
Analytics 1.0 had to innovate to gather all the information from operations, with the help from mainframes and later PCs; going from paper to computers meant a huge advantage in reducing costs, improving efficiency and controlling companies; as Davenport recalls, the most important task was always asking the right questions, to establish which information we needed to be gathering and analyzing, since these were such arduous, time and resource consuming tasks.
Most large companies use these tools in some way, although Business Intelligence has really mostly meant reporting about past operations, which is very useful but a long way from actual predictions. This sort of data gathering, reporting and analysis is still beyond the means of most small and medium sized businesses.
Analytics 2.0 means Big Data: suddenly we have tons of information available, and not just from inside the company; we could have the information for weather patterns, pay days and shopper visits to our stores; and we have the means to gather and process all that information, going from single local servers to many on the cloud, with innovations like in memory processing. Some companies such as Google, UPS and the port of Hamburg have thrived by using Big Data
Analytics 2.1, which Davenport sees as 3.0, is about the use of Big Data and the change it requires from companies to include useful information into their products. It’s useful as it completes the information loop, but it’s more of a 2.1 change because we are still talking about what companies do with all this information with respect to their internal operations and in some select cases about their products, which can result in a huge competitive advantage like for Google and Amazon, and does require changes in the organization; but there is a bigger and altogether different phenomenon going on, and that is how information access is changing our own customers.
On the web our customers are discovering many more aspects related to our products, and they are finding about them from other consumers. In many instances the purchase decision is taking place without any intervention from brands or retailers: our customers find which products best serve their needs among themselves and only contact us at the moment of purchase. Their experience is taking a whole new life after the sale, as they exchange impressions and experiences, which affect our future sales.
In the “old” 1.0, 2.0, 2.1 mentality we can track customers just like we can track our products and operations, with tons of information and processing power, to then seek to improve the efficiency of our transactions; and this is very useful and profitable, but it doesn’t deal with our customers’ new expectations. And those expectations aren’t only about our products but also about ourselves.
A useful real life example example is the poor girl who found herself looking for certain products which taken together denoted an upcoming baby: the data collected allowed Target to send her pregnancy related coupons, which had an adverse effect on her as (a) it angered her parents, which she hadn’t informed, (b) probably made her feel uneasy about being spied on, and (c) probably put her (and others who read the story) off shopping at Target: great use of statistics, complete marketing fail.
What makes the failure even worse is that our customer is more than willing to tell us what she’s looking for, if we only know how to listen and help her find what’s best for her, which goes way beyond price cuts, coupons and promotions; in fact she’s more than willing to help our other customers find what’s best for them. Making customers feel like they’re spied on and used is exactly the opposite of what’s required, which is to earn their trust, make them feel safe and valued. We need to take into account our customer’s expanded experience and our own place in it.
The wording in the NY Times magazine story is telling: Target was fishing for times when customers were “vulnerable to intervention by marketers” and wondered “How do you take advantage of someone’s habits without letting them know you’re studying their lives?”. If it isn’t already clear, customers don’t want to feel vulnerable or that they are being taken advantage of.
We don’t need “data scientists”, we need to know who our customers are, what they are looking for, why, how our product responds to their needs, how they find it, what they do with it; in other words we need to go back to the basics of marketing, now on the web. Business analytics and big data can indeed help, but they are only ingredients in the long term relationship we need to build with our customers.
|Analytics 1.0.||Analytics 2.0.||Analytics 3.0.|
|Name.||Data Warehousing, Business Intelligence.||Use Big Data||Our customer's use of data.|
|What it does.||Gather and analyze operations data.||Vast data sets, much more powerful number crunching.||Understand how our customer uses data.|
|What we can use it for.||Manage operations better.||Incorporate useful info into our products.||Respond to our customer's new expectations.|
We can take these steps to respond to our customer’s new expectations:
- Establish a centralized way of learning about our customers, including all touch points from analytics and Big Data. This is also the first step for other projects such as CRM, customer satisfaction and the web.
- Establish how our customer’s expectations are changing, and how we need to respond.
- Establish who we want to be for our customers on the web, not just what info we can gain from them.