The only way to develop an operational segmentation scheme is to 1) be already "in the ballpark" with a reduced set of key drivers  that link to real-world outcomes and 2) measure those key drivers accurately (see the next benefit).  Our New Generation Values-Based Market Segmentation (NGVBMS) approach does both.

Research has now made it clear that simply asking customers what they like does NOT cut it. In contrast, our segmentation protocol  requires the consumer to make the same TRADE-OFFS in survey that they make in real life.  In this way consumers reveal their TRUE values.  Next, of course, one must accurately SOLVE for those values. We pioneered our advanced utility calculation approach 20 years ago...and it still leads the industry in accuracy.

Segmentation uncovers the combinations of key drivers that are most attractive to consumers. The problem? Conventional "one-size-fits-all" segmentation tends to produce sub-optimal segments. To develop the most actionable segments requires a true segmentation decision support tool, one that: 1) executes multi-algorithmic clustering 2) identifies best solutions using expert systems 3) makes  recommendations and 4) allows the user to redefine the parameters that define "best."  All these features (and more) are built into our advanced segmentation decision support software, SegmentSolve.™

Once the best segments have been uncovered, we import them  into our product and service decision support tool,  ProductSolve™. Quite importantly, these tools contain threshold data...data that identifies how much change is required to bring a consumer over a real-world threshold (purchase, up-sell, cross-sell, etc.)

Developing 1) value-based segments and 2) importing them into ProductSolve™ allows us to identify which specific existing or new products or services will be most attractive to each segment. One of the most powerful real-world outcomes built into ProductSolve™ is the likelihood of purchase (LOP) simulator.  LOP simulation (coupled with imported value-based segments) provides the most powerful current insight delivery  platform for capturing new customers within each and every segment.

Two of the most powerful real-world outcomes built into ProductSolve™ are switching and blunting.  Switching simulation identifies the product configurations that will maximize switching to your product or brand. Blunting, in contrast, identifies how the strategist can protect any product from competitive attrition. By importing the value-based segments developed by SegmentSolve™ into ProductSolve™ these outcomes can be extended to every relevant segment in the market..

One of the most powerful advantages to developing value-based segments within a framework of decision support is that previously separate decision support outcomes can now be integrated.  An example? InnovationSolve™, our innovation decisioning platform can accurately locate both brands and demand on the same landscape.  This means that we can now import each value-based segment into InnovationSolve™ and perform Segment-Specific Innovation (SSI).

One of most powerful capabilities build into our Product Design  protocol is the ability to perform Marginal Benefit (MB) Modeling.  The MB model combines a real world outcome model (e.g. new customer capture, likelihood of purchase, switching, retention, etc) with two types of cost data, cost to implement (CTI) and cost to serve (CTS) data.  By using CTI and CTS, the Marginal Benefit Model finds strategies that maximize real world outcomes at the lowest cost.  When value-based segments are imported into the MB model, we can uncover the best "bang for the buck" for each segment.

The strategic capabilities of our segmentation practice are competitively unparalleled (maximizing purchase, maximizing switching, and maximizing competitive blunting for each segment.)  But there is still something missing....tactics. Since the central decisioning platform for tactical market deployment and sales capture is the datamart (or cloud server) how do we get the power of value-based segmentation onto the datamart?  The answer is our datamart enhancement vertical, and its Attitudinal Imputation Modeling (AIM) protocol. AIM uses multivariate imputation to generalize the segments developed from the survey segments onto every record in the datamart.