As cloud companies grow, the discipline of sales segmentation becomes more important. Sales segmentation is the discipline of focus – focusing sales resources on the right customers. As companies grow they generally expand serving more customers, of different sizes, in more markets, through more channels, with more offerings. This expansion leads to greater sales model complexity. Job specialization becomes critical to maintain effective sales focus. Proper sales segmentation drives decisions on routes to market, channel selection, sales motions, sales roles and ultimately account and buyer targeting.
Getting it right matters. As cloud companies mature, getting segmentation right matters more and more. Sales segmentation differs from market segmentation in that market segmentation typically helps define target markets at a macro level while sales segmentation is much more granular, often defining focus down to the account level and even the buyer level. Segmentation models generally follow a maturity curve for the business, growing more sophisticated as companies get larger and more complex. The value of proper sales segmentation can be monumental. After all, the sales effort is expensive, as much as 50 percent of ACV bookings in the start-up and volume growth phases of a cloud computing company. Guiding sellers to the right targets can pay huge dividends. Conversely, lack of segmentation or poor segmentation can be extremely costly resulting in low productivity, high rep turnover, and even high customer churn.
Figure 1: Market Segmentation vs. Sales Segmentation
Evolve as you go. The discipline of sales segmentation should evolve and grow to match the needs of your business. Most companies begin with a straightforward approach based on observable factors. We’ll call this “Phase 1.” As the business expands, additional segmentation criteria on buyer behavior and sales potential are added to further segment the market and drive seller focus (Phase 2). Ultimately, some companies go one step further to add customer lifetime value (CLTV), often in the form of a complex algorithm, to fine tune their sales segments (Phase 3).
Phase 1: Segmentation based on simple observable factors. Most cloud companies today use this approach. Simple, easy to acquire and track data, such as company revenue and number of employees is used to segment customers based primarily on size. As cloud companies gain traction in the market place, they may add to this by targeting select industries based on their IT spend. Companies that focus their efforts initially on the high end of enterprise, i.e., Fortune 1000 companies (such as Jive Software, Lithium Technologies, and ServiceSource) will likely favor direct, field-based, high touch sales models with inside roles for lead gen, customer support and renewals/customer success. Meanwhile companies with an initial focus on SMB or seeding strategy may be more indifferent initially and pursue a viral or brand marketing strategy to drive initial growth (such as Box, LinkedIn, and Amazon Web Services) with a predominantly inbound/outbound inside sales model. For instance, a large infrastructure company we recently worked with was built from the ground up with an inbound model serving SMB customers. We began by understanding their current customer base by revenue and made recommendations on how to separate their core inbound business and develop a proactive outbound acquisition engine.
Phase 2: Segmentation based on buyer needs and algorithm-based TAM. Many cloud companies are quickly outgrowing their initial sales segmentation models, requiring more sophisticated approaches in order to drive greater levels of sales productivity at the same or reduced sales costs. Initially this can take the form of more and more granular segments based on traditional metrics (revenue, employee count) – instead of just broad segment buckets separating Enterprise, Mid-market and Corporate (companies like Salesforce.com, for example, divide Corporate into multiple tiers). However, the other dynamic that develops as companies grow is the need for continued upsell and cross-sell. As the offering set expands, company size alone may not be the best indicator of opportunity. Growth, particularly in existing accounts, may be tied to more specific factors, requiring more sophisticated sales potential estimation modeling for total available market (TAM) at the account level. This analysis may inform a variety of sales investment decisions including deployment of various overlay specialist roles (product, technical, solution, success) or channel partner programs. One cloud company takes their membership data to drive insights into expected spend within their prospects and account bases. They use these account level insights to drive decisions around marketing programs, quota allocation, territory design, and sales resource allocation.
Phase 3: Segmentation based on buyer needs & algorithm-based CLTV. Some cloud companies are beginning to develop even more sophisticated segmentation strategies built on customer lifetime value (CLTV). In some cases this is a sign of size, scale and maturity – the company has enjoyed success that enables it to take a longer view on investment and growth. In other cases, the CLTV approach is an imperative even at an earlier stage of the business, given the nature of the product or service, i.e., a company pursues a market share gain strategy to attain number 1 or 2 market position at the expense of short term revenue or cash flows. In order to get a good understanding of CLTV models, these companies will want to look beyond the cloud at more mature industries, such as insurance, where companies have taken customer lifetime value into consideration for years. This customer segmentation by CLTV helped them decide which types of customers to actively pursue and would even drive pricing in the form of customer premiums.
Balance sophistication with simplicity. One thing as consultants that we find regarding segmentation work – it’s easy to over-engineer your model. Just because we live in the world of big data doesn’t mean it’s all useful. In some cases highly sophisticated models can produce incredibly simple solutions. One website analytics company segments and prioritizes accounts based on an algorithm driven by website traffic and the presence of certain “analytics friendly” website tools used on company sites to laser focus their selling efforts on accounts that will have a much greater propensity to use their product. This is an example of a sophisticated model that generates an easy to use target list for sales. In other cases, like in the case of behavioral profiles, the segmentation model may be elegant, but ultimately impractical to execute against.
How do you segment customers? Have you outgrown your sales segmentation model?
Learn more about sales segmentation and Cloud Sales.
Originally published by: Dale Chang