Category Archives: Marketing Analytics

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

Big Data – big deal or big hype?

Big Data is a reality.  Big data is also big hype. So where is the real value in the Big Data hype?

Like it or not, Big Data is a reality marketing and other organizations are facing today and will face well into the future. According to the IBM Global Technology outlook, it is estimated that data growth will explode exponentially from 3,000 exabytes today to 9,000 exabytes by 2015. (I had to look up the word exabyte!) So what do we marketers do about incorporating this new source of data?  Where are the best use cases?

We are faced with a challenge – Big Data alone does not help us.  We need ways to get value out of the data.  The challenge is that traditional analytic tools are not exactly suited to take advantage of Big Data.  Analytics vendors are developing offerings in an attempt to keep up with the ever changing needs.  With that said, we need to be thinking about where it fits in.

For me, the best use case is squarely in the camp of customer analytics.  I love my data from the marketing automation system.  It tells me a great deal about the patterns and behaviors my customers and prospects display.  But it only gives me a piece of the puzzle.

As I think about my customer marketing initiatives, I need to be able to prevent customer defection (commonly known as “churn”). By leveraging unstructured data (call center notes, customer interactions, survey responses…) with internal structured data to detect & proactively mitigate factors that lead to defection,  I can better design marketing programs to avoid that “aha moment” when customers realize they no longer want to do business with satisfaction surveyus versus defecting to a competitor.  Conversely, applying the same approach can help marketers identify the “aha moment” which leads to improved customer loyalty

So, is Big Data hype or is it really something we marketers can benefit from?  If marketing organizations do several things, I believe it will improve marketing’s ability reach the target audience with more appropriate offers.

 

  • Big Data platform:  Do not look at Big Data as this mysterious beast that can solve world hunger!  It should be considered an additional data source which can augment existing data sources such as CRM, ERP or marketing automation data.  If you are a marketer, you should be connecting with your IT team to determine how to incorporate   this data into your existing data.
  • Analytics: Do not forget the analytics!  Discover, visualize and explore big data alongside traditional information to drive action and share your insights with others. Employ predictive analytics to identify patterns within the data leading to more accurate answers.
  • Answers!  Ultimately, it is what you do with the data versus the data itself! Use Big Data combined with traditional data sources to deliver more accurate answers by analyzing, predicting and automating decisions.

Connect with me to continue this discussion:  @BrendanRGrady

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.

Can your CMO justify his seat at the board room table? Or is he still scrambling to justify marketing’s spend?

Too many marketing organizations rely on the fuzziness of marketing results without being able to confidently stand up and say:  “Look, this is what we spent and here is the revenue we got in return!”  According to the 2012 IBM CMO study, 63% of CMOs believe ROI will be the most important measure of success over the next 3 to 5 years.

In today’s economic climate, marketing leaders need to be able to confidently report on their results to justify their seat at the table.  We have all heard that analytics are key to improving performance but what does this really mean for marketing organizations?

As a former Director of Marketing Operations, I was challenged to help my CMO answer some difficult and challenging questions.  When asked to answer critical questions around ROI, I would struggle to find answers.  In the early days in my role, I would reply..let me go get that information for you.  I would inevitably return with a spreadsheet that none of the leadership team would believe.  Now imagine hundreds of spreadsheets floating around with different sets of results.  This led to many interesting and heated conversations….about the veracity of the data versus discussions about business performance.

I soon discovered the magic of Business Intelligence which enabled me to confidently answer leadership’s questions.  CWTo address the challenge of multiple versions of the truth we embarked on a journey which would change the marketing performance conversation forever.

Our journey began by defining the standard metrics and KPIs which would form the common language across marketing and sales.  This was by far the most arduous and difficult part of the initiative.  I would submit that it was the most important step in the entire process.

A "dashboard" is like a speedometer ...
A “dashboard” is like a speedometer that marketing professionals use to report marketing performance.

Once we had gained agreement it took about three months to provide a worldwide marketing scorecard to track performance against KPIs, 5 roles based dashboards to the leadership team, standard demand generation reports for 120+ demand generation specialists with the ability to do ad-hoc analysis to understand why certain marketing activities performed well while others did not.

So what changed?  The conversation about performance completely changed…for the better.  The marketing and sales teams defined a common language about how to measure the business.  With agreed upon KPIs, Metrics and a single, trusted version of the truth, the marketing organization focused on what matters – the marketing activities are performing and which are not.  In the end, by using the data in front of us and acting on it, the marketing organization improved open and click through rates, pipeline creation and conversion to revenue.

If you would like more information about this journey please contact me via Twitter @BrendanRGrady