Tag Archives: Big Data and Analytics

Finally! Predictive Analytics even I can use!

When I was running marketing operations, I had hired a statistics expert to come into the organization to help me make sense of all of our marketing data.  As I worked with this excellent “stats guy”, I realized that predictive analytics can be pretty darn complicated and required a more specialized skill set than I had originally thought.

This all changed on Tuesday June 11, 2013.  IBM announced the availability of IBM SPSS Analytic Catalyst – a statistician in the software – which makes predictive analytics on big data available to mere mortals like myself.  I was admittedly skeptical that this new offering could actual make predictive analytics accessible.  There had to be some gotcha….  well, I was wrong (not something I admit often!).

So, what does this thing do exactly? Let’s say you are a marketer and want to understand which customers would likely “churn” or leave for your competitor.  This is where Analytic Catalyst comes in.  There is a three step process to get the answers you need.

  1. Add your data (csv file)
  2. Select the field you would like to predict
  3. Review plain English results

Sounds simple?  Well it is.  Review a demo here to see how this can help marketers, customer service teams, sales and other organizations find the small data within the big data!

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Marketing Win Revenue = Autopsy on a dead guy!

Marketing organizations are not exactly known for their desire or ability to measure the outcomes of their activities.  As a former director of marketing ops I ask myself:  Is it ability?  Is it lack of desire? Or, is it simply that marketers just do not know where to start?   As with any complex organizational discussion, it is likely a mix of the three.  My experience leads me to believe that most marketers just do not know where to start.  And those who have started are likely choosing all the wrong key performance indicators and metrics.

When I was still in my marketing operations role,  I would frequently hear from the marketing teams about how much win revenue we got from our marketing programs.   We were very fortunate that our organization had standardized our reportingCWprovided analysis capabilities to our program managers and the management team with standard dashboards for a single version of the truth.  Managing your business via lagging indicators is fine when you hit your target which we frequently did.

Still, something troubled me.  We knew what had happened and why but still could not answer what WILL happen.  Essentially, we could do an autopsy on a dead guy but could not predict what would kill him…Something had to change.

As a B2B marketing organization we subscribed to Sirius Decisions demand waterfalldemand waterfall In spite of this, our key performance indicators, metrics and business intelligence platform continued to reinforce the behavior that win revenue is king.  Our CMO was no longer content with seeing the results at the end of every quarter.  He wanted to know within a reasonable margin of error (+/- 10%) where we would end up each quarter.  We needed to change our behavior to do this, we needed to change our metrics…moving up the waterfall all the way to inquiries…and beyond.

Applying Predictive Analytics to the Problem

We were swimming in marketing data – contacts, inquiries, marketing qualified leads, sales qualified leads and win revenue.  What we needed to do is figure out what patterns lead to a person progressing all the way through the demand waterfall.   By applying predictive analytics, we were ultimately able to predict within +/- 5% how much win revenue would come from our marketing activities.

We took our contacts, marketing outreach data (emails, web, live events), inquiries, pipeline and win revenue and brought this data together in a predictive model. SPSS-Modeler This predictive model identified those contact job titles, marketing offers and marketing channels which would yield a successful marketing inquiry.  From here, we were able to extend the model to be able to forecast which contacts would lead to inquiries and which of those inquiries would convert to marketing and sales qualified leads.  With this knowledge in hand, we were able to conduct “what if” analyses and predict the optimal mix of contacts, tactics, and channels to meet or exceed targets.

Changing Behavior

Now that we were able to predict the mix of contacts, offers and channels, we decided to change the way we measured marketing moving WAY up the demand waterfall.  We implemented the following metrics:

  • # of contacts within each segment (industry and geo)
  • # of net new contacts added to the database (as determined by email address)
  • # of inquiries
  • Conversion % from inquiry to marketing qualified lead
  • # of marketing qualified leads
  • Net new pipeline created ($)
  • Conversion % from marketing qualified lead to sales qualified
  • Acceptance % by sales rep

Notice – no win revenue.  As a marketing organization we believed marketing is responsible for finding new names and delivering net new pipeline while sales is ultimately responsible for revenue.

We updated our reports and dashboards I spoke about in a previous post to reflect these new metrics and assigned targets to each marketing team based on our predictive models.

My thought: I would rather know early on that I was off target versus having to do the post mortem…what are your thoughts? Send me your thoughts @BrendanRGrady on Twitter.

Would you have married your spouse if you only knew his or her address?

Marketers claim to know their customers because they have captured demographic and historical transaction information about them but would you have married your spouse based on age, height, address and the fact that they bought shoes once?  I think not.  Why should marketers think this enough to truly address the needs of today’s customers?

Many marketers are doing a very good job an incorporating traditional data critical to being able to target marketing efforts.

Data such as:

  • Self-declared demographic information
  • Marketing inquiries
  • Sales leads
  • Orders, payment history

This information is a great place to start.  There is no doubt that applying advanced analytics help marketers find patterns and trends while also predicting what is likely to happen next.  

Think about being on your first date with you spouse.  The conversation starts with the basics:

First Date

  • Where are you from?
  • Where do you live now?
  • Where did you study?

But quickly moves to:

  • What is your opinion of the President?
  • What do you do for fun?
  • What do you really dislike?

Now think about applying this in a business context.  As a marketer, I would like nothing more than to put an offer in front of customers and prospects that would not only speak to their business need but would also speak to personal likes.  How can you do this?

digital thumb print

Every interaction is an opportunity to get to know your buyers better.

Traditional interaction data from a marketing automation system such as Unica, Eloqua, Marketo or Neolane is a valuable source of information about  buyers’ behavior.  By capturing all interactions, regardless of channel, allows marketing organizations to apply predictive models to predict which customers are likely to respond to marketing offers via which channel and how frequently.    Using predictive models as part of a broader  customer analytics initiative helps marketing organizations identify which buyers to target and personalize offers for cross and up-sell opportunities

Great, we now know which offers my buyers will likely respond to and how frequently!  Now, go back to my first example – the date.  Getting to know your buyers personal preferences allows you to gain a deeper understanding of customer attitudes, preferences and opinions to make them part of the decision making process. Think about collecting customer opinions, attitudes and interest via surveys or data collection.  Use the interaction opportunity to capture a hobby or other personal activity.  Then apply this in your marketing activities.  If your buyer likes golf, find a way to incorporate it into your outreach (e.g. my marketing team has used direct mail/dimensional mailers giving away a free driver!).  

Social Media is another data source which can provide tremendous insight into customer opinions, both positive and negative.  Apply social media analytics to get the real opinion of your products to ultimately engage brand advocates and detractors in a conversation. CI Social media analytics allows organizations to capture consumer data from social media to understand attitudes, opinions and trends.

They key here is not to look at each of the pieces of data as stand alone pieces of information.  It is about combining them to get a “thumb print” which identifies the uniqueness of an individual.

Please feel free to connect with me on Twitter to discuss further @BrendanRGrady