You need not always build your analytic tools, sometimes you should buy in. If the chosen application does what you need that often makes good economic sense... as long as you know what you are buying.
Let's be clear, an Analytic name does NOT mean there are any real Analytics under the hood.
For many managers, Analytics is akin to magic. They do not know how an analytics application works in a meaningful way and have no real interest in knowing. At the same time, there is no business standard for what makes up "forecasting", "inventory optimization", "cluster analysis", "pricing analysis", "shopper analytics", "like products" or even (my favorite) "optimization".
Don't buy a lemon!
One promotion-analytic tool I came across recently proudly proclaimed that you (the user) could calculate the baseline and lift for each promotion however you saw fit and then just enter the result into their system. They presented this as a positive feature, but calculating a meaningful baseline and lift is the difficult part!!
I've seen similar approaches for:
- off-shelf alerting tools that ask you how long of a period of zero sales is abnormal (so they can report exceptions)
- supply chain systems that need you to enter safety-stocks or re-order-points (so they can figure out when to order).
- assortment optimization tools that want you to input product substitution rates.
Hmmm, is a car without an engine still a car?
Many applications use pseudo-analytics.
After all, how hard can it be? "cluster analysis" , that's finding groups of things right? I reckon I can figure that out, no stats required. Yeah, right, of course you can... FYI - meaningful, useful clusters may be a little more difficult. It's not that cluster analysis is particularly hard, but neither is it something you can knock together without the right tools or any statistical understanding.
Sadly, I have seen real world examples of pseudo-analytics too in pricing analytics, off-shelf alerting, demographic analyses, inventory optimization and forecasting.
The right tool for the right job.
There are many good analytic applications available, but you can still make it useless if it does not suit the task you have in mind. Using a time-bucket oriented optimization program to schedule production runs with sequencing comes to mind. OK, relatively few people are going to understand that one and it's not a DSR application, but it is real, the software vendor did not come out shouting that there would be a problem and 2 years down the line that project was abandoned.
Are DSRs worse than other applications?
I think this kind of feature-optimism, is a general issue in buying any analytic app but my perception is that it is a bigger problem in the DSR space. Perhaps because the DSR is trying to offer so much analytic functionality to so many functional areas? Is a DSR really going to handle forecasting, pricing-analytics, cluster-analysis, weather-sensitivity-modeling, promotional analytics, inventory optimization, assortment selection and demographic analysis (note - not a complete list), all as packaged software, for $50K a year? Not unless they can scale that investment across a huge user-base. Some will be good, others not so much - be warned.
Spotting a lemon
An expert in the field (with analytic and domain knowledge) can spot a lemon from quite a distance. If you do not possess one you would be wise to invest in some consulting to bolster your purchasing team. For those applications that pass the sniff-test, the proof of any analytic system is in it's performance.
Define rational performance criteria, test, validate, pilot and never, ever, ever rely on a software vendor ticking the box in your RFP.