Now with the 9th and final installment discussing Customer Experience Indexing (CEI™) as a way to measure, plan and act on customer feedback.  (#1) (#2) (#3) (#4) (#5) (#6) (#7) (#8)

CEI is a metrics-based way to drive revenue growth from customers you have, those you’ve lost and one’s you’ve yet to win. In drops 1-8 we’ve given CEI use cases for:

• An expanded Net Promoter-type way to calculate and measure satisfaction + prompted + unprompted customer advocacy
• Applying metrics for better account-by-account management planning
• Building lenses for better strategies and tactics for up-selling, cross-selling and renewals.
• Using metrics for Reference Account Management and sorting a top 10 list of best customer references, and why.

Last week’s rant aside, I heard some heartwarming feedback during my phone rounds last week on Reference Account Management. Sorting a top-10 list using the aforementioned steps is great way to have a constantly regenerated, rules based index of advocates who are not only loyal for reasons intangible, but are also qualified “satisfactioneers” from a statistical lens measuring service fundamentals such as quality function, value, basic expectations, length and frequency of engagement. And as you are able, you can even create sub lists for different verticals, channels, etc. for more precise matching of current prospects with current customers who can and want to help you win deals.

Taking this one step further, what if you took all of the companies on your top-20 RAM list and created a rules based profile that paints the picture of what a perfect opportunity looks like for your sales team? No guessing! If you find and start prospecting companies who match up with your most loyal and satisfied customers you’re not only communicating where your message is most likely to resonate, you’re also creating mindshare and giving your company a strategic messaging and positioning edge segment by segment (directly relating to where you have the most traction). Look at this as a snapshot of your winning market segments and the activities that contributed to these wins, thus arming Marketing and Sales teams with the road map to further success.

Is this a shameless plug for ReachForce’s Insight SaaS? Yes it is. But it’s still a golden opportunity to ask and answer the following:

1. Are you marketing to the right companies and what rules do you use to make this determination?
2. Are there trends in your sales funnel that you are not capitalizing on?
3. What kinds of leads move through the sales funnel the fastest and generate the most revenue?
4. Can you look into their sales funnel and identify current trends. By analyzing opportunities in the sales funnel in real time, marketers are able to adjust programs on-the-fly to help keep deals moving to close.

These are all questions marketers ask themselves as they are developing lead generation programs. And by combining CEI metrics with applications such as ReachForce Insight, marketing and sales teams can finally agree on winning target markets and focus lead generation efforts at other companies that match the same profile.

On with the 8th in a series discussing Customer Experience Indexing (CEI™) as a way to measure, plan and act on customer feedback.  (#1) (#2) (#3) (#4) (#5) (#6) (#7)

In case you’re just dropping in … as B2B marketers we know that our businesses are fundamentally made up of three types of customers. The ones we have, those we’ve lost, and potential accounts doing business elsewhere. CEI is a metrics-based planning tool for driving revenue growth from all three of these targets and over the past few weeks we’ve been working our way down the following outline:

1. CEI Initiative Planning
2. Optimizing the flow of both loyalty and satisfaction feedback
3. 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 plan and deliver action plans aimed at reshaping customer attitudes and opinions
6. (We are here) Using the data to locate new prospects using rule based company profiling and role-based targeting

As of last week we’ve gathered then used CEI response data for:

• An expanded Net Promoter-type way to calculate and measure satisfaction + prompted + unprompted customer advocacy
• Applying metrics for better account-by-account management planning
• Building lenses for better strategies and tactics for up-selling, cross-selling and renewals.
• Using metrics for Reference Account Management and sorting a top 10 list of best customer references, and why.

A Rant About Reference Account Management – cont’d

In my view, good Reference Account Management is perhaps one of the most overlooked things about B2B sales and marketing today. It’s seems to have been subverted by what the 1990s called “relationship selling” or “relationship marketing.” (By the way I am so tempted to try out a new acronym “RAM,” but I won’t).
After last week’s post I devoted some time on Google checking on as many high quality Sales, Marketing and Account Management job postings as I could stand. I had hoped to call some folks up to talk about how different companies are using CEI-type metrics and try to get some understanding of who is responsible for them. After a while I logged off in dismay. I’d read more than forty job descriptions and only one mentioned anything at all about the act of –or responsibility for (call it what you may)– maintaining an index of Sales-ready reference accounts.

As a sales person this finding (unscientific, yet time consuming) makes me feel needy. As an account manager it makes me think nobody is looking. As a marketer I just feel dirty. Even after the much needed influence of Fred Reichheld’s “Ultimate Question” (note to self: call Howie about game show idea) and Net Promoter Scores ― it pains me greatly to think this means that if we asked 1000 companies who their top-10 sales ready customer references are (and why) possibly only 20 could produce the information. Wow.
Because of this I’m sort of ranting this week.

If the discipline of stock managing Sales-ready references is being ignored at your company – especially in an economy where heroic customer experience is required – fix it, or buckle your seat-belt please. I say this because I believe a proper tool for this type of microanalysis and segmentation is the real brass ring for customer experience professionals.

To explain, let me bust out some John Lennon and ask you to imagine what you can do once you’ve managed your CEI metrics into a real-time data driven Reference Account Management list:

• Match prospects you are selling to with just the right customer references during the sales cycle
• Align customers with other customers for:
  • Account assignment groupings
  • Sales plans and quotas
  • Geo-analysis
  • Vertical-analysis
  • Predictive analysis
  • Campaign planning, newsletters, webinars, user conferences, PR etc.
• Align product roadmaps with prioritized customer needs
• Anticipate customer needs
• Use rules-based market profile of your most satisfied and loyal customers to frame market-to-market searches for new prospects

My point is this is that all of the above mentioned stuff sounds great if not essential ― and I think the last 7 posts I’ve done on this subject show that this is not so much about rocket science as it is about ownership and effort. Every company that is serious about growth (sometimes survival) needs to have a CEI-type initiative in place as a part of the organizational DNA. To that, I now add that these metrics are a tool to be used for holding the right people accountable for the right things. Knowing why customers become genuinely loyal to a supplier has everything to do with taking actionable intelligence from the bottomless well of information that is “the customer experience” and plying it back into the strategies and tactics that make your company tick.

Sales organizations that rely too much on relationship selling may not be as inclined as others to take a metrics driven approach. In fact, some studies say that in sectors such as manufacturing just more than half are less likely to consistently use comprehensive customer experience metrics as a part of their prospecting strategy. I think that could be a reason why American manufacturing is getting its collective butt kicked.

Stay tuned for more next week.

This is the 7th in a series discussing Customer Experience Indexing (CEITM) as a way to measure, plan and act on customer feedback.  (#1) (#2) (#3) (#4) (#5) (#6)

Here is the outline we’ve been following:

1. CEI Initiative Planning
2. Optimizing the flow of both loyalty and satisfaction feedback
3. 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 plan and deliver action plans aimed at reshaping customer attitudes and opinions
6. (We are here) Using the data to locate new prospects using rule based company profiling and role-based targeting

So far we’ve gathered then used CEI response data for scoring to examine three existing customer scenarios as examples:

• An expanded Net Promoter-type way to calculate and measure satisfaction + prompted + unprompted customer advocacy
• Applying CEI-metrics for better account-by-account management planning
• Building CEI-lenses for better strategies and tactics for up-selling, cross-selling and renewals.

Next on the list is to take a look at using CEI response data to help locate, target and engage with net-New prospects.

Reference Account Management

The most obvious and useful way CEI scoring benefits the new sales process is the buttoned down way it sorts advocacy dynamics and pinpoints which current customers would make the best references based on data analysis, not on someone’s opinion. There is nothing more powerful from a news sales perspective than having a well stocked supply of sales ready references. It happens every day across the world, thousands of times a day ― a sales person bursts into the marketing or account manager’s office needing three references to connect with their prospect. Not only is the list of needed attributes arms length, but it all needs to happen before tomorrow afternoon. Sound familiar? Yes it does.

This scenario takes us back to the first exercise we did for determining what a customer’s advocacy rating is. Remember it’s a matter of reading how a customer feels about their entire experience with your company using a scoring schema that takes metrics from both qualitative (loyalty) and quantitative (satisfaction) feedback into account. So if asked to produce recommendations about what customers should be the best sales-ready references we’d produce response scores rendered from a two-step lens build that would look something like this:

Step 1 Top 10 Sales Ready Reference Accounts

Once each row on the customer list has an assigned CEI Advocacy Score, simply sort this column in descending order and in combination with the column for customer response time to your survey plus overall satisfaction scores, plus Key Weight. This  (if you remember back to the 1st and 2nd posts in this series) is because the survey invitations were sent as an integrated campaign, i.e. first an email, then another, then a phone call reminder from the account manager, then another from an executive, then perhaps another email, etc., thus determining how quick to respond each survey taker was. It stands to reason that someone who responded quickly in combination with high scores from Advocacy, Satisfaction and Key Weight are going to be a good sales ready reference account.

Step 2 Top 10 Sales Ready Reference Accounts

So the above mentioned sort produces a top 10 list based on:

Next week we’ll cover ways to build rules-based profiles of your most successful customers and your relationships with them and then use the data to score how well new company targets match the rules.

On with the 6th in a series (#1) (#2) (#3) (#4) (#5) discussing Customer Experience Indexing (CEI^TM) as a way to measure, plan and act on customer feedback. Many thanks to those already asking questions or offering comments.

As B2B marketers we know that our businesses are fundamentally made up of three types of targets. These are the customers you have, those you’ve lost, and potential accounts who – so far – have decided to do business elsewhere. CEI is a metrics-based planning tool for driving revenue growth from all three of these targets.

Over the past few weeks we’ve been working our way down a list of 6 areas that frame up a basic CEI initiative set up:

1. Planning
2. Optimizing the flow of both loyalty and satisfaction feedback
3. Analysis of feedback and calculation of actionable CEI metrics
4. (We are here)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

Last post we discussed the Key Weight – or how long and how often does a customer “experience” your company, and how account-by-account Key Weight scores should influence how the entire body of survey response data gets interpreted.  Not taking newness or lower frequency of use metrics into account leaves open a broad chance that important dangers or opportunities get overlooked as you update Account Management plans (which we’ve demonstrated using example analysis from a recent ReachForce Customer Experience Survey results i.e. Key Weight + Data Accuracy + Project Manager Expertise). See (#5). To build on last week’s discussion, let’s take a look at some other lens-building using Key Weight as the prime factor:

Customer Categorization – by putting customers with common Key Weights into separate buckets you’ll get true apples-to-apples comparisons in terms of stack-ranking other response scores. Examples of the kinds of questions/scores that can apply were given in drop (#4) but here they are again:

It is advisable to bucket in a way that creates a “big” middle (i.e. Top 15%, Upper-middle 35%, Lower middle 35%, Bottom 15%). This is because the main gist of the plan is to create a process of continuous improvement that pulls customers (see below) from Bucket D into C, C into B and B into A.

If you think back to what a Key Weight is comprised of (combined scores of “how long” and “how often”) pulling customers towards the top bucket means two really good and important things need to happen … i.e. keeping the account active/open and increasing the amount of meaningful contact (defined as “use”) you have with them. Again, don’t think of Bucket D being all bad and Bucket A being all good. It really just gives you an instrument to ascertain the degree relationship maturity and of the certainty you should have for response scores to other questions.

To build another example lens, let’s say you’ve come to the stage of your 2009 Account Management plan where you need to sort a list of customers ranking how likely they are to renew their contract with your company and what you need to do to maximize your probability of success for > 80% of them.

Again, we’ll use some data taken from ReachForce’s Q4 2008 Customer Experience Survey and see what we come up with – starting first with rating each customer Key Weight category and cross-tabbing scores for “Will you renew?” “Repeat Purchase Experience,” “Data Value ROI,” “Compare with other Vendors” and “Usage Experience.”

The questions/responses I’ve used as cross-tabs to track down answers to the questions at hand (how likely they are to renew their contract and how do we maximize probability of success for > 80% of them) have to do with stated intent to renew (cross tab 1), how good our renewal + up/cross sale experience is (cross tab 2), is the customer recognizing ROI (cross tab 3), how do we stack up versus alternatives (cross tab 4) and usability (cross tab 5). I think this mix gives us a clear view of the renewal picture.

Because increasing probability for successful renewals is in large part about eliminating barriers, what I initially look for in a chart formation such as this [above] is ascending point values as they are an indication of trouble. The logic is that scores in all columns need to get better (and show up as descending) as length of engagement and frequency of use increase.

For example:

• The ascending values in the Cross Tab 2 column tell me that we are not up-selling or cross-selling as well or effectively as need be … i.e. newer customers coming fresh out of the sales pipe (Bucket D) seem happy and impressed (9.6 avg.) erosion starts to occur (9.3, 9.1, 8.9 C-A respectively). It’s clear we need to write a remedy for this into our 2009 Account Management plan.
• Although scores are pretty high, the lack of consistency in the Cross Tab 5 column may indicate that the high-touch nature of our on-boarding process – where planning, kickoffs and software activations are the norm – may lose a bit of its shine as time goes by. This begs the question, are we being complacent with older customers, or are they distracted – and by what? To make sure this does not become a barrier to future renewal campaigns we need to take a close look at how to mitigate this trend.

In all other cases the cross-tab columns and the Analysis Score (last column) on the chart above are pretty high and have a nice descending order of value. But as an Account Management planner I can use CEI to continue drilling into things that I normally would not see that need tactical consideration:

For example:

• Bucket B is nice and full (45%) and we likely need a specific program to increase conversions to Bucket A before it becomes a log jam. Again, placement in any of the buckets is based on length and frequency of engagement and should not be seen as an index of good or bad – rather, as mature relationships versus less mature relationships. So in 2009 we need plans that focus on increasing mindshare and quality time with Bucket B accounts.

But notice a couple of additional things about Bucket B:

• The lowest two cross-tab average scores for this group are:
  • Data Value ROI (8.9)
  • Compare w/ other vendors (8.9)

While 8.9 for both questions are pretty solid averages, to a careful Account Management planner this is still a notable indication that work needs to be focused/done in these two areas to reduce the chance of them becoming obstacles to our 2009 customer renewal plan.

More next week. As always, thanks in advance for your questions and comments.

On with the 5th drop in a series (#1) (#2) (#3) (#4) discussing Customer Experience Indexing (CEI™) as a way to measure, plan and act on customer feedback. All questions and comments are extremely welcome and I do appreciate those who have already jumped in.

Working our way down a list of six areas:

1. Planning
2. Optimizing the flow of both loyalty and satisfaction feedback
3. Analysis of feedback and calculation of actionable CEI metrics
4. (We are here) 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

In the last installment (#4), we rolled out some quick analysis of data from a recent ReachForce Customer Experience survey ― zeroing in on the different angle we took by measuring prompted versus non-prompted advocacy  — and what differences exist between companies that are a reference account (92% spending x amount) versus a full blown advocate (73% spending y amount).

It is, glory be, nice to have such high numbers for both reference accounts and advocates at ReachForce. And it’s even nicer to know that as satisfied customers (x) evolve into proactive advocates (y) they also tend, as explained last drop, to buy software and services more often, and in greater amounts (#4). So as a Customer Success team planner it becomes imperative to first figure out why it happens – and set out a continuous plan of improvement to make it more predictable.

To get there, we first establish the Key Weight – or how long and how often does a customer “experience” your company? I have been scolded for this approach in the past by people who say it’s not fair or smart to weigh qualitative feedback from new or infrequent customers more lightly than older ones, and I understand the concern. But I don’t think of it as lower weight = less important (all feedback is important) ― rather, lower weight = less sure.

To work a very simple example, if planning 2009 MBOs for our project managers requires a comparison of two key accounts assigned to the same Project Manager, the following analysis might be used to help step us in the right direction:

For key account planning these numbers tell me the project manager (PM) assigned to these two customers is delivering high marks on both quantitative (data accuracy?) and qualitative (expertise?) fronts ― and with two very different scenarios (new customer/once per month and old customer/once per week). This is good. But because the lower of the cross tab scores are from the (quantitative) ‘PM expertise?’ question, I can foresee the MBOs assigned to the PM in the case of both customers will be warm, fuzzy and relationship directed in order to bolster the customer’s perception of the PM’s expertise. Or maybe the PM gets more training. And closing the planning loop, I’d probably use “Moving Customer 2 up to weekly engagement” as another measurable objective. A higher level of meaningful contact would help.

And as you can see by looking at the % Analysis Scores above, without factoring the Key Weight in the above example, you’d only be fooling yourself about Customer 2 data accuracy and PM Expertise ratings, because you would not be taking newness, or lower frequency metrics into account and an important danger or opportunity might be overlooked. To some, planning account by account MBO strategy this way may seem overly analytical, but I have found no better way to customize and create MBOs to and pinpoint action plans right where the rubber hits the road.

To get some ideas about which cross tab questions to use as lenses for various situations, think of it in terms of Value Delivery (quantitative) versus Obstacles for Value Delivery (qualitative) ― as in our example of Data Accuracy versus Project Manager Expertise ― wherein bad Project Management would be an obvious obstacle to delivering high Data Accuracy.

I’d be happy to provide further example scenarios here, but I think you get the drift. Remember, I think it’s less of a service to create some sort of template, than it is to just spark some thought and let folks craft CEI indexing tools that mean the most to your specific world.

Next week we’ll look at a few more of these CEI ‘planning lenses.’

I’ve been talking to a lot of people these days about database hygiene. Many have asked for best practices amounting to a “get well, stay well” healthy data routine. We do have a great white paper on this subject, Is Dirty Data Sabotaging Your Marketing Results?

One step is to make sure that users of your CRM system have a handy/easy way to “flag” inaccurate contact records.

If you are a salesforce.com customer, here is a way to help in this area:
The instructions/advice below requires administrative privileges in salesforce.com.

FINDING DIRTY DATA/EXCEPTION REPORTING

1. A field must be designated for data quality/lead quality to be reported through.  For instance, here, we use our “Lead Status” field and have created custom values in the pick-list to accommodate the error reporting.  Example: Bounce Back (Email); Wrong Phone Number; Contact No Longer There, Company Closed/Acquired.

a) A pick-list type field is best for this because it standardizes the options for reporting.
b) We have found it useful to go beyond the simple “Bounce Back, Wrong Phone Number” type of justifications for a contact being wrong and include information such as Company Acquired/Closed (with a custom field allowing users to input the company it was acquired by), Company No Fit: No budget/need, Wrong Contact: Need higher level contact/lower level contact, etc.  Once you have a basic infrastructure for error reporting customizing the field to your organization’s needs is easy.

2. After the field is in place and has begun being used, create a custom report that pulls the data you wish to report on.  It is typically much easier to report on the exceptions (the wrong data) rather than the correct data.

• Go to the “Reports” tab, select “Create New Custom report”
• Choose “Leads”
• Use a tabular report format
• The columns we use are:
• Lead Owner
• First Name
• Last  Name
• Title
• Company/Account
• Lead Source
• Lead Status
• Created Date
• Created Month
• Street Address
• City
• State/Province
• Zip/Postal Code
• Country
• Phone
• Email
• Website
• RF Internal Project (*Custom field, created to denote what project each
• contact came from)
• Nothing needs to be summarized on the standard summary fields
• Any changes in the placement of columns is strictly based on personal preference

For report criteria the following is important:
a) View – choose All leads.  Date/Duration/Start & End Date – leave the standard “Create Date” and “Custom” settings but delete the date in the “Start Date” field – leaving it blank ensures all possible data is captured in the report.
b) Advanced Filters:

• Field – Lead Status (or whatever field you have elected to use to report the error data in)
• Operator – contains
• Value – Whatever error messaging you have input, for example “Bounce Back, Wrong Phone Number, Contact No Longer There.”  If you have used a pick-list to populate the field then you will be able to use the look up (looking glass icon next to “AND”) to select all relevant information.

Here at ReachForce, we run this report on a weekly basis, but companies with less sales reps or reps who make fewer calls won’t necessarily need to run the report that frequently, conversely companies with lots of reps or reps who burn up the phones will want to run an error report more often.

RE-IMPORTING NEW/CLEAN DATA

To avoid lead duplication, it is best to export the bad data with a unique identifier (like the Lead ID provided by salesforce.com) and then re-import  the data using the same identifier. By using the Lead ID, you can ensure that notes and activity history that were previously attached to the record are not lost in the process.

It is important to remember to reset the Lead Status field once the data has been cleaned up (or whatever custom field designates the data as bad) so that sales reps calling on the data know it is ready to be called on again.  Here at ReachForce we also have a “born on date” for our refreshed data.  In that field we import the date that the records were updated in salesforce.com so that the rep calling on the record always knows how current the information is.

After data has been cleaned up, it is important to continue to keep the data up to date, using the error reporting discussed above and setting a time-table for how long it takes for a lead to “expire” allows you to keep all of the data in your database fresh.  For example, if a lead is new on 9/1/2007, it is safe to assume that by 9/1/2008 it might be in need of an update.

DATA SCORING/RECORD COMPLETENESS

We have found it useful to leverage a free AppExchange program put out by salesforce Labs called “Data Quality Analysis Dashboard 1.0″ to give better visibility into the completeness of records and what deficiencies exist. This package installs a set of dashboards and reports that give scores to each type of record within your CRM and then provide high level views of how your records score.  Using this tool can give you a better idea of where your records need improvement and/or if particular types of records are better kept than others.

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.

In my last two posts (#1) (#2) a discussion about Customer Experience Indexing (CEI) as a way to measure, plan and act on both emotional (loyal) and quantifiable (satisfied) customer feedback is underway. Many thanks go out to those who have offered feedback and questions so far. Keep them coming!

We’ve just started working our way down a list of six (I’ve adjusted slightly) areas:

1. Planning
   ->we are here
2. Optimizing the flow of both loyalty and satisfaction feedback
3. Analysis of feedback and calculation “CEI scores”
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

Last week the importance of having an approved plan and budget was stressed. Don’t start something like this unless the leadership at your company is willing to sign on as sponsor and be directly involved long term. The goal is to have sustainable ways to collect customer feedback, as well as having a formal business process to make sure collected feedback is actionable.

Every good CEI plan needs three basic components operating as pistons in a multi-mode data acquisition engine. Simple is better … so we look at them this way (with a few examples):

1. Ways to PUSH for customer feedback
   -customer satisfaction survey – SLA report card – quality/value audits
2. Ways to PULL customer feedback in –
   -customer conference, advisory board, focus group, social media
3. Ways to REPSOND consistently to customer feedback
   -CRM data integration; adjustments to communication, product, service level, project and account management strategies

Programs such as key account groups and client advisory panels will differ greatly in execution but the goals are the same … create a unified approach to the relationship with a particular customer, no matter how many points of contact there are and bring the right resources to the table at just the right time regardless of whether those customers are buying, implementing, maintaining or planning their future.

As I said last week (#2) a good way to get a CEI plan off the ground is with a simple but effective customer satisfaction survey. Here, response rates are king and the kinds of questions asked are important because this sets the waterline for both satisfaction and loyalty metrics for the entire program going forward.

I’m happy to go into greater detail about optimizing survey response rates –just let me know, but basically the tactics listed under item #7 from last week’s checklist (#2) have always worked very well for the teams I’ve been on. For example, we recently achieved 90% response to the 2008 Customer Satisfaction Survey at ReachForce.

Survey writing is next. Remember to use less than ten questions and keep them pithy. Simple language = good. And as a giver of many surveys, trust me – use multiple choice and/or ‘true or false’ as much as possible. Open ended questions make peoples’ hair hurt.

Two types of questions to think about:

1. Service fundamentals (quality function, basic expectations)
2. Advocacy (value, competitive advantage)

‘Service fundamentals’ questions are (logically) where the bulk of your ‘satisfied-type’ metrics will come from. They should be written to solicit quantifiable answers.

Specific example:
“How many times a year do you use service ABC?”

This example locks potential answers down to specific objects and links the all important use-frequency metric that is part of the weighting and scoring stuff we’ll talk about later.

‘Advocacy’ questions are for getting inside the respondents’ head and are often multi-dimension. These are where the bulk of your emotional ‘loyalty-type’ metrics will come from (like Net Promoter). They should be written as an attempt to empathize with the respondent.

Specific example:
“From list below, select the part of your ABC service plan that you DISLIKE most?”
And then for added dimension: “what do you LIKE most?”

In both cases a list of well thought out (and friendly tested) multiple choices will help hone in on what about your relationship is most important and vulnerable to the respondent. People generally ‘like’ (and are loyal to) things that are solving their pain = important to them. They generally ‘dislike’ (and aren’t loyal to) things that miss the expectations mark and force them to find a work-around, or as we say: “leave their pain on the table.”

The same duality is REALLY in play with the “Ultimate” advocacy question = “Would you recommend us to a friend or colleague?” As I was taught as a young dad, “to be really, really sure you have to check their temperature from both ends.” I propose more detail on that, here, next post. Keep the feedback rolling.

Last week I expressed my thoughts about the differences between “loyal” and “satisfied” customers. I give lots of credit to Net Promoter for the role it has played promoting (sorry for pun) the concept of easy to digest relationship marketing metrics. That said – I personally need more than just an advocacy index to create and tune relationship strategy for my company’s customers.

Customer Experience Indexing is how I measure, plan and act on both emotional (loyal) and quantifiable (satisfied) customer feedback. Starting with item 1 below, my next several posts will open discussion about …

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

Customer feedback in the consumer marketing world has become an art form. Comparatively, B2B companies seem to lag far behind in having clear, sustainable ways to collect feedback, as well as having formal business process to make sure collected feedback is actionable.

The best way to get started is to make sure your company’s business leaders buy in and get involved. And the best way to make sure this happens is to write up a plan and make sure it gets budgeted. (Warning — Don’t even start a Customer Experience Measurement initiative if you don’t have this support. Without it, you will do more harm than good with your customers — as well as waste a lot of time and effort.)

While there are many variables to this type of planning by company size, type, etc., this blog series will cover quite a few customer feedback channels. The most common of all is the dreaded “annual customer satisfaction survey” (mine come with a few twists), and for a number of reasons it’s the best way to get a Customer Experience Measurement effort successfully off the ground.

To finish off this week’s post here is a checklist for getting your plan started:

1. Subscribe to an inexpensive online survey tool (a valuable thing you’ll find many uses for).
2. Figure $60 per customer as a good budget for getting the first year of your plan started (very large customer bases may need smaller scale plan)
3. Involve everyone with frequent customer facing responsibilities (Account managers, Project managers, etc.), segment your list up and assign “contact ownership” by role, (not just by account).
4. Meet with company stakeholders to craft role-based, multiple choice questions that need to be asked and answered. (We’ll talk about putting the scoring mechanism in place later). There are two basic types of questions to consider:
   • Service fundamentals (quality function, basic expectations
   • Advocacy (value, competitive advantage)
5. Test the questions on at least 2 friendly customers and ask them what questions they think you should ask.
6. Create and review the invitee list with your assigned contact owners. Usually, I like to keep the invitations focused on people you deal with (by role) at accounts that have been active within the past 12-18 months. Make sure email addresses and other contact information are accurate/current and that all contact owners “agree” (important) that inviting them into survey process is viable (formula is alive + working + accessible = invitee).
7. Clarify objectives, rewards and tactics. I’ve run a few of these initiatives so I tend to aim high.
   • Goals/objectives: If the invitee list is agreed to with the assigned contact owners (step 6), shooting for and achieving 90% response rates within 45 days is highly doable.
   • Rewards: Offer invitees a meaningful reward for taking time to complete the survey. There are many ideas here, but $20* Amazon.com e-gift certificates have always worked wonders for me. Offer the same $20* (per response) reward to the assigned contact owners as a way to motivate them to provide all important follow-up throughout the remainder of the 45 campaign.  *Note that we’ve already used 2/3s of the per customer budget.
   • The following tactics have proven highly successful in maximizing response rates:
     -Don’t ask more than 10 questions
     -Keep questions pithy
     -Avoid soliciting open ended responses as much as possible
     -Schedule email invitations/reminders to avoid heavy traffic
     -Leverage companywide email signature lines with reminders/links
     -Leverage all phone customer phone contact with reminders
     -Include a write up about the survey in your customer newsletter
     -Leverage outbound customer mailing (including billing) with reminders
     -Create fun response rate competition amongst the assigned contact owners
     -Get upper management involved with reminder phone calls, personal emails, etc.

To be continued. Please chime in with your own ideas and thoughts.

My Granddad used to say, when times are tough hang on to those that love you. Is the same true for tough economic times and B2B relationship marketing?

Your market is fundamentally made up of three types of targets. Customers you have, those you’ve lost, and potential accounts who – so far – have decided to do business elsewhere.

This is the first of a serial discussion (please join in) about measuring and connecting some specific customer experience management dots for minimizing customer churn, growing key accounts and identifying new revenue opportunities with companies that share common profiles with those with whom you do well.

Over the decades I’ve devised and managed dozens of customer retention programs. As a deliberate marketing proponent I was an enthusiastic Net Promoter adopter because of its implied relationship with corporate growth and its sheer simplicity – something very appealing when trying to achieve internal buy-in for major (sometimes costly) customer experience initiatives.

But in times like these where the outcome of a company’s customer experience strategy can make or break quarterly revenue plans, a one dimensional measurement such as NPS may help to know how many loyal customers there are, but isn’t very good for knowing about problems or –more importantly– how to fix them.

So while I love the idea of NPS as a simple advocacy index (as well as the role it’s had on increasing the importance business owners now place on these types of marketing metrics) I’ve found it to be just one of the many dots that need connecting to drive revenue growth. The most common mistake in the B2B world today is confusing loyal customers with satisfied customers.

The difference between satisfied customers and loyal customers is distinctly a matter of emotion. And while metrics dealing with both are very different and have unique implications – they are interdependent as two halves – quantifiable (satisfied) – and subjective (loyal) of the complete customer relationship picture.

This means customer feedback must be secured, structured, analyzed and acted upon in both concrete and abstract formats. To this end I have developed an arsenal of best practices that can be used for the following:

1. Optimize the flow of information and feedback that captures both quantifiable and emotional responses to “customer experience” surveys.
2. Analytical methodology for connecting and measuring quantifiable and emotional feedback to determine a “CEI,” or Customer Experience Index for each customer on your list.
3. Templates and guides for using CEI scores to craft short, mid and long term account plans for retention, up-selling and cross-selling.
4. Templates and guides for using highest CEI scores to locate new prospects using rule based company profiling and role-based targeting.
5. Templates and guides for using lowest CEI scores to plan and deliver action plans aimed at reshaping customer attitudes and opinions

Over the next few weeks my blog posts will address these subjects one-by-one. Again, I’d really appreciate your feedback as we go.