Bridge The Marketing Metrics Chasm

A Model to Drive Executive Insight from Department Metrics

Most of us in B2B marketing have a plethora of metrics at our disposal. Typically these metrics are aligned to departments such as web marketing, customer support, field marketing and corporate marketing. The challenge many companies are facing is that while these metrics, individually, are powerful tools for their respective departments, they lack organizational wide relevance. Our CMO’s cannot adjust well and or predict future growth within the context of website “stickyness” or  email click thru rates.  While these department metrics are key to operational performance, the challenge in front of us in Marketing Operations is to lend macro level relevance and business context to their meaning.

In thinking this thru, my belief is that we must first approach this from a business model standpoint. To this end, we know two things; ALL businesses have a customer engagement lifecycle and ALL businesses are changing. With these two things in mind, we might consider a macro model based on these two elements.

Element A: Customer Engagement Lifecycle Indices

Definition:  Four key Indices which are based on a number of “children” department level metrics. Each index represents a key stage in Customer Engagement.

Element B: Business Logic (Metric Weighting)

Definition:  A layer between department metrics and Customer Engagement Indices which allows for configuration of “weighting” to specific “children” metrics.

The Pyramid Visual

With these two elements in hand, we are now ready to pull it all together into an Executive view. One visual which I find useful is that of a pyramid. The bottom layer of the pyramid represents the department metrics, the middle layer represents customer engagement indices and the top layer often represents revenue or brand equity. This top layer is what we can regress with the indices in order to test the correlative effect of an index on the future value of say revenue or brand equity (more to come on that topic in a future blog post).

Color for Full Effect

Finally, we add color to represent the attainment of success within BOTH department metrics as well as engagement indices. Remember that index achievement is simply a function of the weighted achievement of its “children” metrics.

It is helpful to use a standard, for example, color code based on quartiles of achievement:

0-25% of Goal – RED

26 – 50% of Goal – ORANGE

51 – 75% of Goal – YELLOW

76+ of Goal – GREEN

Below is a sample visual of the finished product (non Pyramid Visual for sample).

Quick Executive Analysis of the above sample may conclude:

1)      “Our biggest challenge is maintaining relevance. This stems from poor data and relevant content assets. Further investment in data quality and content creation are a top priority.”

2)      “As a result of our relevance challenges, we are receiving poor SEO Visibility and therefore our Inbound Marketing goals are at risk.”

3)      “Once we get customers in our pipeline, we are very efficient. Therefore our sales enablement and supporting sales processes seem strong.”

4)      “Generally customers like our product, they adopt it and are often referable. This however does not seem to be equating well to sentiment which may mean a broken social media channel.”

5)      “All in all, regression analysis has proven that relevance is key to maintaining brand equity. As such, currently brand equity is at risk of collapse if the relevance problem is not solved.”

I hope you find this model simple to use and flexible to meet your organizational needs. As always, please provide feedback with constructive criticism and/or successes you have had implementing within your organization.

Ryan

(ryanrjkelly@live.com)

Driving Sales & Marketing Alignment thru Predictive Analytics

It seems not that long ago that Marketing was tasked with operating as a profitable business unit. With that came “Dashboard Mania”…essentially marketing’s response to executive calls to exhibit ROI.

We have all worked hard to build the supporting data model as well as visual output to prove our existence to the organization.  Now – just as we are starting to figure this out and begin to think we can put it in cruise control for at least a year, our fearless leaders not only want us to prove ROI on what we have done but literally create a crystal ball to prove future ROI on what we are going to do!

Predictive analytics is simply our ability to identify where changes in certain variables will affect other variables. More formally, predictive analytics represents our ability to demonstrate strong correlation between a dependent variable and its list of predictor(s).  Sport books have been doing this for years – they have massive servers which literally crunch thousands of dependent and predictor variables to shoot out the score of a football game. If it rains, and the 49ers are at home and the QB is less than 25 years old and the visiting team is from east of the Mississippi, then there is a 65% chance that they will win the game outright.

My research into predictive analytics within marketing led me to a space filled with credit card companies, insurance companies and other B2C financial institutions. It makes total sense given their business model is better suited to the art than say an enterprise B2B software company where coupon offers to increase customer retention is a little harder to execute J.

This being said, once digging a little deeper, predictive analytics can and should be alive and kicking in the B2B space. A logical area of highest impact can be found within the alignment of our organizations sales and marketing departments.

Sales & Marketing Alignment: CRM Adoption

Lets face it – we all, in some form or another, are hamstrung by the fact that our data layer (primarily CRM) is reliant on a sales force which is time constrained and simply not wired well to maintain. The net result is that we are strapped with this handicap thru our campaign planning, execution and metric gathering efforts.

Now – let’s consider how predictive analytics may change this equation. What if an account executive was told that if he/she maintains these 6 data points on their contacts (monthly) – then we will have an engine which will guarantee them more $$ per hour spent on account acquisition and management.

In other words, “John Doe, you maintain these 6 fields monthly, it will take you an extra 10 minutes per month (per contact), the result of this is that we can guarantee you an increase in 25% closure rate with the leads we give you which results in additional commission of $45,000 per year for you based on our average sales price.

This may be the holy grail that FINALLY drives the symbiotic relationship of sales and marketing data to fruition.

Sales & Marketing Alignment: Funnel Forecasts & Campaign Planning

Our sales funnels are no longer a function of opportunity pipeline within our CRM systems. As sales & marketing alignment as become part of the DNA of best in class organizations, funnels are now a shared entity for both departments starting with sales stages far before a contact becomes an opportunity.

Thru the use of BI tools, we can accurately represent the conversion rates and velocity thru these lead stages whereby providing a more predictive view of the actual revenue – farther out in time.

More interestingly is that thru the use of correlative tools, we can also understand the influence of campaign types on users moving thru lead stages. With this data in hand, we have a powerful tool in our campaign planning process. By looking at bottlenecks in our funnel and understanding what campaign types drive conversion, we can predict the success of our campaigns and ultimately map our campaign investments better.

In summary, I hope that this blog entry has provoked greater thinking around predictive analytics particularly within the alignment of our sales & marketing efforts.

Part two of this entry will focus on predictive analytics and its ability to help our organizations drive customer loyalty and ultimately shareholder value.  

Sail Around the Perfect Storm: Lead Scoring

Lead Scoring to Achieve MAP ROI While the Organization Cleans up the Closet

Implementing a Marketing Automation Platform (MAP) is the B2B Marketing equivalent of “Skeletons in the Closet.” Few other implementations will uncover the grisly details the majority of organizations have with respect to poor sales & marketing process, data and alignment. Plugging an Automation Platform on top of these “Big 3” is the perfect storm for failed expectations, a lack of adoption and a tough way to earn a living.    

The reality is that these are issues which are not easily solved and are dependent on areas of change within the organization far removed from the MAP “owner”. So – is all lost? Are we destined for mediocre results until these issues get solved?

The answer is; YES, unless you do something about it.

Fortunately, there are a number of areas within the automation space where significant ROI can be achieved even when the underlying Big 3 are not solved. One such area is that of lead scoring, the underpinning of MAP platforms.

Lead Scoring is a great example where you have the ability to choose your destiny. The standard scoring implementation of explicit/implicit scores and a generic lead “fence” is great (and needed) however make sure you temper the expectations of management with respect to ROI.  Bad data will inhibit your ability to score well and poor processes and/or alignment will surely affect your ability to correlate conversion rates as well as change the dials to optimize your scoring model in the future.

Now, let us consider additional implementations of scoring which will augment what is outlined above to enhance your MAP ROI even in the face of Big 3 challenges.

If we break lead scoring down to its core, it is essentially the representation of the sales disposition of a contact. By sales disposition, I am referring to product areas of interest, degree of interest and explicit alignment to target personas. 

We can likewise describe the goal of our marketing campaign efforts is to positively affect the sales disposition of a group contacts. This can occur by enhancing their engagement within specific areas of solutions to their pain(s) as well as acquiring contacts who have explicit alignment to our target buyers.

With this mapping in place, we can begin to see how tightly related we should consider lead scoring and our campaign processes.

Campaign Targeting

The majority of organizations will target their campaign(s) based on predetermined notions of industry, job title, etc. They will go to their database to pull on these attributes and that becomes their “list” which great riches will be reaped.

There are two flaws in this scenario;

a)      We know our data is poor therefore relying on it for “clean” pulls is not satisfactory.

b)      Within the sales process for complex engagements, we are being disingenuous to ourselves if we believe we can actually predetermine all purchase influencers.    

Now , enter lead scoring. A number of companies I have recently spoken to are now evolving to their next stage of lead scoring; one which has multiple scoring models based on their range of solution offerings. With this in hand, we can start to heat map against contacts who are already showing engagement within a solution area to create our campaign targets. Some companies have gone further to actually set a threshold of existing engagement within a solution area as a base requirement to run a campaign. For example, we will only deploy a webinar effort when we have over 200 active B1 or higher scores within the solution area.

Campaign Measurement

The ability to attribute revenue to specific campaigns is a daunting task within complex B2B purchasing journeys. This problem becomes exasperated with bad data as well as poor processes/alignment – once again the nasty “Big 3” strike.

Might we consider how lead scoring can help solve this problem (or at least be part of the solution). If we think of the goals of a campaign as being to forward the sales disposition of a contact and sales disposition is measured by lead score then can we not create a lead scoring based measurement for campaigns?

If we look at the lead scores and stages of contacts within a particular campaign target and then post campaign review those scores and stages, we can build an attribute model to show the campaign ROI. In other words, to augment typical opportunity pipeline ROI for our campaigns, we can now add ROI within the pipeline PRE opportunity creation – and scoring is a great method to quantify that. With a good understanding of our rates & conversion, we should know that the 25 B1 contacts this campaign acquired is worth xx and the 40 A4 contacts that are now A1 are worth yy. Add these values to your standard campaign reporting dashboards to provide even greater insight to ROI.

I will look to start to write more of these as time permits. Feedback is always appreciated….Cheers.

Ryan