Predictive Analytics at a Global Computer Manufacturer

Imagine you are a global computer manufacturer. You may or may not own the factories that produce your hardware, but one thing that you do own for sure - which is a significantly more complex beast - is effectively one massive data factory.

Consider for a moment all the data being produced by your organization, for your organization, and being dumped into your organization's prolific data warehouse system(s):

• Marketing Data
• Quality Data
• Efficiency Data
• Sales Data
• Returns Data
• Web Generated Data
• Call Center Generated Data
• Analyst Data
• Investor Relations Data
• Social Media Data
• Maintenance Plan Data

And so it goes on...this barely scratches the surface of the data generated, but let's use it as the starting basis to make some sort of a point, which has been a long time coming...

As this is a blog on predictive analytics, the question returns as always, to how could predictive analytics be useful to such an organization, and why?

The overarching answer to the question 'why?' is likely something approximating: because our organization has developed a keen interest in looking forward, to what is about to happen, rather than just reporting what has happened in the past. This idea probably has general appeal to just about any sober executive, conceptually it is a great thought, after all.

But grandiose as it is, it doesn't really solve any specific business problem, rather it is just the theme that unites the solving of hundreds, thousands, or millions of business problems. Therefore, it worthwhile to consider 'why?' on a more micro-level. So we will address this relative to the data types listed above (very superficially) to gain a flavor for what predictive analytics can achieve:

• Marketing Data - Which prospects are most likely to respond to an offer for product x? Which prospects are likely to respond favorably to an up-sell? How can I predict which segment a customer falls into with only partial information about them?
• Quality Data - What are the big predictors to quality problems? Given scenario a,b,c,d - what is the quality level I can expect at the end of the production line?
• Efficiency Data - What are the big predictors of more efficient manufacturing lines? Given scenario a,b,c,d - what is the efficiency level I can expect at the end of the production line?
• Sales Data - How much are we likely to sell this day, week, month, quarter? What are the big predictors of sales volume?
• Returns Data - Exactly which order to which customer is most likely to get returned? (A scored list for each daily shipment, with statistical probabilities of likelihood of their being a return issue attributed to each shipment, so that interventions can be placed on those classified as high risk). What are the big predictors of goods being returned to us?
• Web Generated Data - Which behaviors on the website are most likely to lead to a purchase? Therefore which customers are more likely to purchase? Do we treat them differently?
• Call Center Generated Data - Given variables a,b,c,d and e - what are my staffing requirements going to be?
• Analyst Data - Which analysts are responsible for giving us the most coverage? How do I score analysts in terms of influence/importance?
• Investor Relations Data - How do I score investor relations inquiries in terms of importance, so that the appropriate level of support can be provided to each inquiry?
• Social Media Data - Are there any correlations between the fire hose of social media data being generated and business objectives? Can I predict future events from specific occurrences in the blogosphere?
• Maintenance Plan Data - Which customers are most at risk of not renewing their maintenance plans? Which prospects are most likely to purchase maintenance plans? What are the biggest drivers of people not renewing? Which customers should I not aggressively offer maintenance plans to, as they are statistically unprofitable?

Most large organizations, and global computer manufacturers are no exception, have a dedicated predictive analytics department. Most frequently it is tucked away in a deep dark corner of the organization. They are usually tooled up with software from companies like IBM/SPSS or SAS - both great companies. The people in this department are clever people, and well trained in the software (which is what it requires - this software cannot be learned in a quick tutorial - try days to weeks). These people are also constantly troubled by the morons in the business who want to try some new predictive analytics project. Like any problem of resource allocation if your issue is not considered an organization priority it will likely not get done - even if it would help your specific department immeasurably. That is if you even know that this predictive analytics capability exists within your organization - trust me, most people don't.

Generally its fair to say that the performance of predictive analytics takes on the aura of alchemy - something you most certainly don't want to try at home, and most probably are too intimidated to even try at work... A regular business analyst dare not even consider it. So you either convince the predictive analytics department to do it, or put it in the 'too hard' basket.

At 11Ants Analytics we've been busy painting a new future, it looks something like this:

'Put the analytics capabilities into the hands of the people most motivated to solve the problem'

Imagine a world where anyone that could use Excel could perform advanced predictive analytics. The capability becomes widely deployed to subject matter experts (e.g. marketing, quality, etc) through-out an organization, even if they knew nothing about data mining and statistics.

That is what 11Ants Model Builder delivers - it is an incredibly easy to use Excel Add-In that allows anyone to practice advanced predictive analytics, and be up and running within minutes. Science fiction no more - go check it out.