# CoolData blog

## 28 July 2010

### Applying predictive modeling to Phonathon segmentation

Filed under: Annual Giving, Donor acquisition, Phonathon, Segmentation — Tags: , — kevinmacdonell @ 8:05 pm

Segmenting the prospect pool is where the rubber hits the road in modeling for the phone program. Too bad there’s no road map!

When I was a prospect researcher working in Major Gifts and doing a little predictive modeling on the side, I was innocent of the intricacies of Annual Giving. I produced the models, and waited for the magic to happen. Hey, I wondered, how hard can it be? I tell you who’s most likely to give, and you get to work on that. Done.

Today, I’m responsible for running a phonathon program. Now I’M the guy who’s supposed to apply predictive scores to the Annual Fund, without messing everything up. What looked simple from the outside now looks like Rubik’s Cube. And I never solved Rubik’s Cube. (Had the book, but not the patience.)

In the same way, I have found that books and other resources on phonathon management are just no help when it comes to propensity-based segmentation. There seem to be no readily-available prototypes.

So let’s change that. Today I’m going to share a summary of the segmentation we hope to implement this fall. It’s not as detailed as our actual plan, but should give enough information to inform the development of your own plan. I’ve had to tear the Rubik’s Cube apart, as I used to do as a kid, and reassemble from scratch.

Click on this link to open a Word doc: “Phone segments”. Each row in this document is a separate segment. The segments are grouped into “blocks,” according to how many call attempts will be applied to each segment. Notice that the first column is “Decile Score”. That’s right, the first level of selection is going to be the propensity-of-giving predictive score I created for Phonathon.

It would seem that shifting from “traditional” segmentation is just as easy as that, but in fact making this change took a lot of hard thinking and consultation with others who have experience with phonathon. (*** See credits at end.)

Why was it so hard? Read on!

The first thing we have to ask ourselves is, why do we segment at all? The primary goals, as I see them, are:

1. PRIORITIZATION: Focusing attention and resources on the best prospects
2. MESSAGING: Customizing appeals to natural groupings of alumni

There are other reasons, including being able to track performance of various groups over time, but these are the most important at this planning stage.

By “prioritization,” I mean that alumni with the greatest propensity to give should be given special consideration in order to maximize response. Alumni who are most likely to give should:

• receive the most care and attention with regards to when they are asked (early in the calling season, soonest after mail drops, etc.);
• be assigned to the best and most experienced callers, and
• have higher call-attempt limits than other, lower-propensity alumni.

The other goal, “messaging,” is simple enough to understand: We tailor our message to alumni based on what sort of group they are in. Alumni fall into a few groups based on their past donor history (LYBUNT, SYBUNT, never donor), which largely determines our solicitation goal for them (Leadership, Renewal, Acquisition). Alumni are also segmented by faculty, a common practice when alumni are believed to feel greater affinity for their faculty than they do the university as a whole. There may also be special segments created for other characteristics (young alumni, for example), or for special fundraising projects that need to be treated separately.

The “message” goal is often placed at the centre of phonathon segmentation — at the expense of optimizing treatment of the best prospects, in my view. In many programs, a rigid structure of calling by faculty commonly prevails, exhausting one message-defined pool (eg. “Law, Donors”) before moving on to the next. There are benefits to working one homogeneous calling pool at a time — callers can more quickly become familiar with the message (and objections to it) if it stays consistent through the night. However, overall gains in the program might be realized by taking a more propensity-driven approach.

Predictive modeling for propensity to give is the “new thing” that allows us to bring prioritization to the fore. Traditionally, propensity to give has been determined mainly by previous giving history, which is based on reasonable assumptions: Alumni who have given recently are most likely to give again. This approach works for donors, but is not helpful for segmenting the non-donor pool for acquisition. Predictive modeling is a marked improvement over giving history alone for segmenting donors as well: a never-donor who has a high likelihood of giving is far more valuable to the institution than a donor who is very unlikely to renew. Only predictive modeling can give us the insight into the unknown to allow us to decide who is the better prospect.

The issue: Layers of complexity

We need to somehow incorporate the scores from the predictive model into segmentation. But simply creating an additional level of segmentation will create an unreasonable amount of complexity: “Score decile 10, Law, donors”, “Score decile 9, Medicine, non-donors”, etc. etc. The number of segments would become unmanageable and many of them would be too small, especially when additionally broken up by time zone.

I considered keeping the traditional segments (Faculty and donor status) and simply ordering the individual prospects within each segment using a very granular score. This would require us to make a judgment call about when we should drop a segment and move on to the next one. The risk in doing so is that in leaving it to our judgment, we will either drop the segment too early, leaving money on the table, or call too deep into the segment before moving on. Calling alumni with a decile score of 7 before at least one attempt to ALL the 10s runs counter to the goal of prioritizing on best prospects.

So, what should we do?

The proposed new strategy going forward will draw a distinction between Prioritization and Messaging. Calling segments will be based on a combination of Propensity Score and Donor Status. More of the work involved in the “message” component (based on Faculty and past giving designations) will be managed at the point of the call, via the automated calling system and the caller him/herself.

The intention is to move messaging out of segmentation and into a combination of our automated dialing system’s conditional scripting features and the judgment of the caller. The callers will continue to be shown specific degree information, with customized scripts based on this information. The main difference from the caller’s point of view is that he or she will be speaking with alumni of numerous degree types on any given night, instead of just one or two.

Our system offers the ability to compose scripts that contain conditional statements, so that the message the caller presents changes on the fly in response to the particulars of the prospect being called (eg. degree and faculty, designation of last gift, and so on). This feature works automatically and requires no effort from callers, except to the extent that there are more talking points to absorb simultaneously.

The caller’s prospect information screen offers data on a prospect’s past giving. When historical gift designations disagree with a prospect’s faculty, the caller will need to shift gears slightly and ask the prospect if he or she wishes to renew their giving to that designation, rather than the default (faculty of preferred degree).

Shifting this aspect from segmentation to the point of the call is intended to remove a layer of complexity from segmentation, thereby making room for propensity to give. See?

‘Faculty’ will be removed as a primary concern in segmentation, by collapsing all the specific faculties into two overarching groups: Undergraduate degrees and graduate/professional degrees. This grouping preserves one of the fundamental differences between prospects (their stage of life while a student) while preventing the creation of an excessive number of tiny segments.

Have a look at the Excel file again. The general hierarchy for segmentation will be Score Decile (ten possible levels), then Donor Status (two levels, Donor and Non-Donor), then Graduate-Professional/Undergraduate (two levels).  Therefore the number of possible segments is 10 x 2 x 2 = 40. In practice there will be more than 40, but this number will be manageable. As well, although we will call as many prospects as we possibly can, it is not imperative that we call the very lowest deciles, where the probability of finding donors is extremely low. Having leftover segments at the end of the winter term is likely, but not cause for concern.

This is only a general structure — some segments may be split or collapsed depending on how large or small they are. As well, I will break out other segments for New Grads and any special projects we are running this year. And call attempt limits may require rejigging throughout the season, based on actual response.

New Grad calling is limited to Acquisition, for the purpose of caller training and familiarization. I prefer Renewal calling to be handled by experienced callers, therefore new-grad renewals are included in other segments.

Other issues that may require attention in your segmentation include double-alumni households (do both spouses receive a call, and if so, when?), and creating another segment to capture alumni who did not receive a propensity score because they were not contactable at time of model creation.

Potential issues

Calling pools are mixed with regard to faculty, so the message will vary from call to call. Callers won’t know from the outset who they will be speaking with (Law, Medicine, etc.), and will require multiple scripts to cover multiple prospect types. Training and familiarization with the job will take longer.

The changes will require a little more attentiveness on the part of call centre employees. The script will auto-populate the alum’s preferred faculty. However, the caller must be prepared to modify the conversation on the fly based on other information available, i.e. designation of last (or largest) gift. The downside is that callers may take more time to become proficient. However, the need to pay attention to context may help to keep callers more engaged with their work, as opposed to mindlessly reading the same script over and over all night.

Another potential issue is that some faculties are at a disadvantage because they have fewer high-scoring alumni. The extent to which this might be a problem can only be determined by looking at the data to see how each faculty’s alumni are distributed by score decile. Some redistribution among segments may be necessary if any one faculty is found to be at a severe disadvantage. Note that it cuts both ways, though: In the traditional segmentation, entire faculties were probably placed at a disadvantage because they had lower priority — based on nothing more than the need to order them in some kind of sequence.

As I say, this is all new, and untested. How large or small the proposed segments will be remains undetermined. How well the segmentation will work is not known. I am interested to hear how others have dealt with the issue of applying their predictive models to phonathon.

*** CREDITS: I owe thanks to Chris Steeves, Development Officer for the Faculty of Management at Dalhousie University, and a former Annual Giving Officer responsible for Phonathon, for his enthusiastic support and for certain ideas (such as collapsing faculties into ‘graduate’ and ‘undergraduate’ categories) which are a key part of this segmentation plan. Also, thanks to Marni Tuttle, Associate Director, Advancement Services at Dalhousie University, for her ideas and thorough review of this post in its various drafts.

1. I was with you right up until you mentioned that you moved the messaging to the point of the call. We’re a manual system and I’m concerned that might be too unwieldy to manage. It sounds like it’s worth exploring, however.

Comment by Jen — 29 September 2010 @ 12:08 pm

• I hear you. There is no single solution for every phone program. Certainly, applying predictive scores to a manual, paper-based system is a whole lot easier if the constituency is not divided up into separate faculties that all require their own messaging. One school, one fund, one message = easy to carve up by propensity to give. Usually it’s not that simple, though. If your callers work from prospect cards, and your predictive scores are in the database, you can arrange to have the score printed directly on the card and then divide them up amongst callers who are each pre-assigned to a faculty (or other natural constituency); each caller stays on the same message for the night, lessening the need to have multiple script versions in front of them. But I imagine that the range of potential solutions is myriad.

Comment by kevinmacdonell — 30 September 2010 @ 6:47 am

2. Thanks for the credit Kevin. I just found this when I was reviewing your site for references for my datamining project!

Comment by Marni — 23 October 2010 @ 8:26 am

3. […] segmentation criteria (eg., faculty and past giving status), but they had lower priority. (See Applying predictive modeling to Phonathon segmentation, 28 July […]

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