The B2B Lead

Customer Experience Index Scoring – Part 4 – B2B Marketing and Sales Tip #178

Continuing with the 4th in a series (#1) (#2) (#3) discussing Customer Experience Indexing (CEI) as a way to measure, plan and act on customer feedback. Again, thanks to those who have offered comments and questions so far.

Working our way down a list of six areas:

  1. Planning
  2. Optimizing the flow of both loyalty and satisfaction feedback
  3. (We are here) Analysis of feedback and calculation of actionable CEI metrics
  4. Using the data for short, mid and long term account plans for retention and growth
  5. Using the data to locate new prospects using rule based company profiling and role-based targeting
  6. Using the data to plan and deliver action plans aimed at reshaping customer attitudes and opinions

To get the very best read on how a customer feels about their entire experience with your company, a scoring schema needs to be created to take metrics from both qualitative (loyalty) and quantitative (satisfaction) feedback into account. And it’s more important to get the idea and then craft a schema attuned directly to your situation than it is to try and create some sort of template. The key is to start producing metrics that people can use. There is nothing more boring than a report about customer experience unless the data comes within a highly actionable framework. To get there, let me share some tactics taken from a very recent Customer Experience Survey of ReachForce customers (shameless company promotion).

First, when we set the Customer Experience Survey up, we asked questions from both ends: quantitative (designed to detect technical satisfaction) and qualitative (designed to detect perceptions and feelings). Since this was ReachForce’s first major CEI initiative we paid special attention to creating solid questions for benchmarking – against which future survey results will be compared:

Quantitative question examples

  • Repeat purchase
  • # Data quality issues
  • Data value (ROI)
  • Frequency of use
  • Length of use
  • Have you recommended
  • 3 most important purchase criteria

Qualitative question examples

  • Purchase experience
  • Usage experience
  • Repeat purchase experience
  • Expertise
  • Compare with other vendors
  • Overall satisfaction
  • Would you recommend
  • Will you renew
  • Would you seek our brand for related services

Again, keep the wording simple and short when writing the questions and use multiple choice or True/False response, except for text boxes to capture responses for purchase criteria. Don’t be tempted to assume your own multiple choices to list for purchase criteria – Get these from your customers in their own words. More on this later in the series.

Here are a few more thoughts to consider while you are deciding on questions to ask.

  • Repeat purchase / frequency of use / length of use metrics will help calculate truer weights for responses to qualitative “feelings.” The more a customer has purchased from you, the more weight their feelings should have.
  • Ask the ‘Overall Satisfaction’ question up front as a way to set the best survey taking tone for the responder. Doing this immediately plucks the respondent’s overall impression of your company right out of the air – then builds upon it as subsequent questions are answered. This is a good way to get very honest answers.
  • Use skip logic to route newer customers away from questions about repeat purchase or renewals. In general, avoid questions that make the respondent feel like you are trying to up-sell or cross-sell.
  • On qualitative questions, give responders a way to respond in varying degrees. It’s hard to get into someone’s head with just Yes/No. For example, if you ask, “Would you recommend our company?” some good variances might be “Absolutely,” “Likely,” “Maybe,” “No.”

The point of the whole effort is to target actionable data discovery to bolster a competitive advantage both by leveraging the positive and finding/fixing the negative. As a simple example the bullets below about recommending ReachForce are simply an expanded take on Net Promoter. The big difference is the angle we took in terms of prompted versus non-prompted advocacy, or as I view it, the ‘gap population,’ — and the differences that exist between companies that are a reference account (92% spending x amount) versus a full blown advocate (73% spending y amount).

  • 92% OF REACHFORCE CUSTOMERS SAY THEY’D RECOMMEND US (IF ASKED)
  • 73% SAY THEY’VE ALREADY RECOMMENDED US (WITHOUT BEING ASKED)

As we do our Customer Success planning for 2009 we know that 92% of customers would act as a reference account if we asked. We also know that 72% have acted as a ReachForce advocate without our asking. Additional cross tab analysis shows that the 19 point gap is comprised of customers who are more satisfied with us from a technical, quantitative perspective than they are from the warm and fuzzy ‘experience’ perspective.

This is huge because now we not only know who they are – we also know what they specifically need from us to take that step up from ‘reference’ to ‘advocate.’ It’s so important because we know (by cross-tabbing these metrics with Customer Lifecycle Value) that the 73% of customers who are advocates also spend more! What better way to fine tune projections for organic growth and cross-sell, up-sell opportunity? More next week. Chime in customer experience geeks.

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3 Responses to “Customer Experience Index Scoring – Part 4 – B2B Marketing and Sales Tip #178”

  1. Deborah Eastman Says:

    Your analysis of customers that would recommend and those that actually have is interesting. I’m curious on whether your recommend question followed the Net Promoter methodology of scoring 0-10?

    Our research has shown that even those that are classified as “Promoters” 9-10, not everyone actually refers. This is due to many factors including personality and brand category. In the credit card industry we found that 53% of Promoters actually referred and in the Computer Hardware industry 78% of Promoters actually referred.

    If you are interested in more information on our research in this area and how to build an economic model for Net Promoter, you can download white papers at: http://satmetrix.com/satmetrix/resources.php?page=4.

    It may be interesting to evaluate whether your 19% of customers that have not referred are classified as Promoters on the 0-10 scale and if they have the personality to recommend without being asked.

    In any case, looks like you are doing something right if 73% of your customers report that they have actually recommended and 92% would recommend if asked! That’s good word of mouth.

    Deb Eastman
    CMO, Satmetrix

  2. Cody @ ReachForce Says:

    Hi Deb,

    It’s an honor to have your comments. Thanks.

    The ‘recommend questions’ (have you, will you) would be scored/valued as follows:

    Will you recommend in the future:
    Definitely 10
    Probably 8
    Might or Might Not 5
    Probably Not 2
    Definitely Not 0

    Have you recommended in the past:
    Yes, without being asked 10
    Yes, after being asked 8
    No 0

    These scores would also baked into a respondent’s ‘total score’ for the entire question set. It’s also used to comprise combined scores for a number of cross tabs we do. For example:

    1-10 scores for Data Value (ROI)
    frequency of use
    + length of use
    + would you recommend
    + have you recommended
    = sum (sort of a panoramic “Uber-NetPromoter” scores that can be parsed to see if longer term or frequent users are responding much differently than new customer)

    I’ll talk about my ideas about trying to profile personality types in future a post. This is where scoring of other types of engagement behaviors (other than survey, e.g. SLA report card meetings, social computing mix, etc.) comes into play.

    Thanks!

  3. Deborah Eastman Says:

    Keep us posted on your findings in this area. We have been doing more research on the area of what we call NetWorked Promoters http://www.satmetrix.com/satmetrix/solutions.php?sub=5. This takes the concept of a Promoter and adds psychographic analysis around those that are credible, connected and charismatic, leading to higher levels of recommendation.

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