Navigating a Myriad of Sales Technology Tools

There are a number of sales technology roadmaps that do a good job of trying to document the abundance of sales technology providers in the marketplace. It is no simple task, as the number of such tools and organizations grows by the day. And, as expected with new product types, many of these companies do not fit cleanly into discrete categories. Ultimately, there will be a market consolidation and a bucketing of functionality. But for now, each solution competes with all of, and none of, the others.

Where does that leave sales leaders? In our 2018 Sales Operations Optimization Study we asked sales leaders what kinds of tools they were deploying (either as standalone modules within CRM or as separate applications integrated into the CRM).

Sales organizations told us that they are using on average 10 sales tech tools, with another 4 planned in the next 12 months. Even so, there is much variance in which tools are being used. In fact, of the 25 tool types that we inquired about, only 4 had greater than 50% adoption. As such, every tech stack looks different, not just in terms of tools selected, but in terms of tool categories.

Top tools being deployed include:

  1. Lead intelligence and lead generation
  2. Social selling and buyer insights
  3. Activity management tracking
  4. Pricing
  5. Forecasting tools

Notable growth was expected in the next 12 months in account and opportunity planning tools, coaching tools, and content management tools to support sales enablement efforts. And organizations are beginning to see some results. Those implementing higher numbers of tools, on average, achieve higher win rates of forecasted deals than those implementing fewer tools. Specific categories of tools whose usage is associated with the highest levels of quota attainment and win rate are:

  1. Account and opportunity planning tools
  2. Gamification/motivation
  3. Partner relationship management (PRM)/channel management tools
  4. Predictive lead scoring
  5. Customer success management

New capabilities in machine-learning and natural language processing, along with the increasing ability to collect data, will continue to create new options for sales leaders to drive productivity. Here are some recommendations for those looking to expand their toolset:

Plan ahead for integrations. Adding applications creates the need for sales enablement and sales operations to proactively and collaboratively build out a roadmap. In the lists above, many of the tools being deployed are point solutions that can create a complicated sales system and force hard decisions and sometimes expensive investments in integration. In addition, some functionality is increasingly available within the core CRM platform. What are originally intended as sales productivity drivers can quickly become confusing when sellers have to navigate too many systems or enter data in multiple locations.

Don’t underestimate training. When we conduct our annual sales enablement study, we commonly find that organizations fall short in key training areas such as social selling, sales methodology, coaching and value messaging. While sales operations may have the primary role in managing the integration to the CRM, installing the tools, and acting as intermediary to corporate IT, it is the sales enablement function that will play a critical role in ensuring those tools actually get used.

Employ solid change management principles. Consider how hard the adoption curve will be to climb (remember how long it has taken to drive CRM adoption!), and determine whether a pilot approach or a full implementation makes more sense. Involve managers deeply in the process. In our 2017 World-Class Sales Practices Study less than half (42%) of organizations said that their managers were successful in holding sellers accountable for tool use. All of these systems depend on data and usage.

Build a roadmap to AI. Organizations seems to be taking two approaches to installing machine-learning based solutions. Some are starting with analytics and rules-based engines and, after enough data is collected to train an algorithm, moving to AI based tools. Others are jumping right in and using AI tools to mine the data they have to get started. There is no right approach. However, the end state should be AI. Straight analytics approaches are hard to continuously improve without heavy user involvement. AI-based solutions continuously retrain the algorithms to better predict results.

Questions for you:

  • What have been some of your more successful change management initiatives? Did they work well enough to replicate? If so, why?
  • What are the biggest productivity issues that you want to address with technology? Specifically, what would be a big win for salespeople?
  • What is the user experience like today? How many tools are touched in the course of a typical sales cycle?


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