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August 10, 2012

14

Improve Sales: Less Big Data, More Big Questions

by Jeremy Arnone

The analytics revolution sweeping across many organizations has yet to be fully incorporated into Sales organizations.  Despite recent progress, having tools like Salesforce.com and Google Analytics is simply insufficient.  Far too few organizations are combining proven Sales approaches with a true analytical mindset to ask and answer the right questions.

We have seen 3 waves of analytics in Sales:  the late 1980s, the late 90’s with Siebel Systems, and more recently, with Salesforce.com and similar tools.  Surveys show widespread dissatisfaction with CRM implementations, and Big Data tools like Salesforce.com are not well utilized or even understood.  Despite this, executives are encouraged to invest just a little bit more – Big Data goodness is just around the corner.

Clearly there’s a major disconnect between Big Data promises and the reality in most Sales organizations today.  Before investing more resources, senior executives should make sure they’re on the right path, by taking the following steps:

  1. Use segmentation to identify customer traits correlated with their KPIs.
  2. Prioritize segments based on the incremental impact from sales.
  3. Tailor KPIs (and salespeople’s incentives) for each segment.

To see how this might work in action, let’s explore a recent engagement where a leading enterprise confronted this situation.  The data is disguised to protect confidentiality.

Challenge

The company has seen impressive growth based on a model of marketing driving leads and sales turning leads into customers.  With growth slowing, it is considering significant investments in marketing and sales, but is concerned about the impact on profitability.  What should it do?

Discovery

We started with a deep dive into the analytics, correlating 3 primary KPIs (conversion, average revenue per user (ARPU), and LTV) with 17 variables contained at the user level within Salesforce.com and proprietary databases.  We used this segmentation to calculate expected values on each KPI based on the type of user.  A sample of these values – conversion rates – is listed below.

Sample conversion rates for several key segments.

While interesting, this analysis begged the question of where the sales team should focus.  To find out, we broke each segment into a control and a test group.  The control group received the standard outbound email and phone contacts from the sales team.  The test group received no outbound contacts from the sales team – essentially Sales was turned off.  The difference in performance is listed below.

Conversion rate differences based on sales contacts vs. no sales contacts

There were several interesting insights.  The first was an eye opener:  The sales team’s impact on conversion wasn’t 20% but only 5%; surprisingly, 15% of customers in the test group converted without contact from Sales.  About half the leads were from Broadcast media, very unqualified, but still took 60% of the sales team’s time.  Leads from online marketing channels had a very high conversion rate when contacted by sales, but saw a more than 50% decrease when not contacted.  And the most active leads – using the product most frequently – actually converted worse when they were contacted by the sales team!

The last step was to move beyond conversion as the sole KPI, focusing on LTV for each segment.  For segments with very low conversion, we moved further up the funnel, testing micro-conversion events (such as a whitepaper download or attending a webinar) and allowing users, with minimal direct guidance, to move down the funnel at their own pace.  For segments with already high conversion, we focused lower in the funnel, exploring ways to increase ARPU (via upsells and cross-sells) or decrease future churn (via onboarding).

Impact

A scoring model was automated within Salesforce.com, with a recommended content and contact strategy put in place for each segment.  The least qualified leads were assigned to an auto-responder campaign, and outbound contact from the sales team was significantly reduced.  This freed up time for the team to focus on other opportunities, including retention, ARPU, and outbound prospecting.  It also saved more than $1M by demonstrating an expanded sales team was unnecessary.  Meanwhile, marketing shifted their mix from broadcast to online marketing channels, increasing ROAS by 120%.

Implications

Many companies are reluctant to tinker with their approach to sales – even minor inefficiencies can jeopardize revenue.  Plus, incorporating new ways of thinking requires coordination across the entire organization, from sales, marketing, analytics, support, and finance –  and the consistent support and direction from the top.  Much easier said than done.

However, substituting analytics for gut instinct and basic CRM reporting helps take the guessing game out of important investment decisions.  Using test-and-control techniques popularized in the direct response world can show the true impact of the sales team and better prioritize their focus and objectives.  Executives need to understand what’s going on within the sales organization, starting with some basic questions around analytic competencies and capabilities.  Rather than invest in systems, tools, and training, many companies would be better off hiring a couple of very inquisitive, analytical problem solvers, and provide them cover to ask all sorts of interesting and (potentially) uncomfortable questions.  The end results will very likely be faster, cheaper, and better than any outsourced or automated solution.

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14 Comments Post a comment
  1. Nick S
    Aug 10 2012

    Very interesting approach, I’ll forward this to our CRM team, I wonder if we have this type of analysis available.

    Reply
    • Aug 13 2012

      Hi Nick. As long as you can track response at the user level, and have a way to set aside a group of users so that they don’t get a contact (this is easy to automate w/in Salesforce, or you can always do it manually) from Sales, you should be able to replicate the analysis. Ping me if you need any additional info – Jeremy.Arnone@Fuqua.Duke.edu.

      Reply
  2. Robert
    Aug 10 2012

    I appreciate the effort to push sales people to think, evaluate and re-evaluate the way they pursue business. We need to continue to drive the profession forward and strive for standards of excellence.

    Reply
  3. Brian
    Aug 10 2012

    CSO Insights just did a study (http://www.1to1media.com/view…. that showed that salespeople are suffering data fatigue — they search as many as 15 different data sources to find information on prospects and customers. Another study (Aberdeen) reported that the average sales rep spends 24% of their time searching for relevant information to prep for sales calls.

    But we have seen the power of Big Data to transform marketing via marketing automation, as all the digital behavior (clicks, opens, views, etc) are transformed into true insight that improves conversion rates and relevance of offers. The same is happening in sales, as platforms emerge to transform the raw data — of internal and internal data — into true insight for sales reps. I think the future is bright for big data playing an increasingly central role in sales productivity.

    Reply
  4. Aug 13 2012

    Just saw your link on HBR, pretty blown away with your approach to calculating impact from the Sales team. Have no idea what our company’s ‘incremental’ ratio looks like, but anything close to what you found here would be incredible. Right now our sales team is credited with the entire conversion but we know that sometimes the customer closes without our help. We just don’t know how many would do so. Can you share more info?

    Reply
    • Aug 13 2012

      Hi Matt. Send me your contact info and let me know what info you’d like me to share. The data itself is confidential but the detail underlying the approach/analysis is something I’d be happy to discuss. My email is Jeremy.Arnone@Fuqua.Duke.edu.

      Cheers.

      Reply
  5. Jan 11 2013

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  6. Jan 14 2013

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  7. Jan 22 2013

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  10. Jan 24 2013

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