AI-supported sales
reps: How to make it work
Most organizations have begun to invest in AI
to guide their sales representatives as it helps organizations stay adaptable
to changing customer needs and evolving markets. AI guided selling usually
takes the form of machine learning generated advice offered to reps on their
CRM or other software. It’s primarily designed to help salespeople stay
organized, prioritize leads, choose the customer most likely to buy for their
next sales call, and so on. When its impact is fully realized, it gives
salespeople more time to sell and information that they leverage to sell more
effectively. I’ve seen solutions like this work, even quite well. With such
great promise, companies are investing in and designing their own custom
solutions. Many start-ups are developing solutions and makers of CRM software
have widgets designed to address AI-guided selling. In my experience most
companies are struggling to implement this approach.
Although most of these initiatives fail, an
investment in AI is more critical now than ever. But why do they fail? After
many conversations with business leaders about these failures, I’ve noticed a
pattern: Many leaders point to absence of sufficiently robust data to fuel
machine learning models. Without enough data, AI can’t make accurate
predictions and if the system fails to make salespeople more insightful or
effective, adoption plummets. Sales organizations find themselves in this
situation for many reasons, from the inability to stitch together a
comprehensive view of customers to transactional data that resides in siloed
repositories, to poor CRM data or the inability to stitch the data together to
enable sophisticated analysis. But it’s almost always more than data that
limits the success of such efforts and solving these challenges are essential
to driving the success of such initiatives. These include alignment across
leadership, leveraging first line managers, picking the right models and
fitting in with current processes.
1.
Align leadership and
evolve the metrics. Alignment among leadership on the vision for how AI will
enable sales is critical to long-term success. This is a journey that requires
alignment around different priorities at each stage. At first, AI guided
selling may be about getting salespeople to try and then adopt the solution,
while over time it may evolve to recommendation acceptance rates and
participation in the feedback loop, and so on. While revenue matters, in the
end, success is about embedding the right habits in place. Revenue and customer
success will follow adoption.
2.
Involve first line
sales managers. The importance of first line sales managers can’t be
overstated. They play several important roles in any sales organization
including but not limited to managing sales representatives, customers and the
business. In AI guided selling, if managers aren’t engaged early and often,
from bringing the solution to life to ensuring that reps participate, adoption
will lag. So thinking through how to get managers aligned with the objectives
of such an initiative and familiar with the details of such an implementation
is critical. And since managers play an active role in guiding how salespeople
strategize and execute in their territories, driving this kind of behavior
change across the sales organization requires the integration of AI in all
rep-to-manager interactions.
3.
The model must fit the
need and be effective. First, models must fit their environments. For example,
when enabling an inside salesperson who has responsibility for a hundred or
more accounts, you’ll need a model that provides the right information about
the right customer at the right time. Arguably, it’s difficult for a sales
person to know and stay in touch with all customers across multiple products so
this functionally is useful. In contrast, when enabling a salesperson with few,
large strategic accounts, you’ll need a model that can coordinate sales efforts
across myriad activities and individuals. Secondly, models must appropriately
balance accuracy and explainability. With the current state of AI, greater accuracy
comes at the cost of sacrificing clarity about why it’s made certain decisions.
(The more complex the calculation, the harder it is to explain to a user with
any efficiency.) If a salesperson receives a recommendation with limited
explanation as to why it’s being made, she’ll be less likely to trust it, which
will harm adoption.
4.
Don’t let data
insufficiency stop you. Yes, better data leads to more robust predictions
but finding adequate data is a journey for most organizations and while it’s a
worthwhile investment, it’s likely to take time. Data insufficiency, however,
doesn’t have to mean you do not begin. There are many paths to addressing this
challenge, including beginning with educated guesswork while a more
comprehensive data journey is considered. For example, organizations can direct
salespeople to call on customers who have not been called on in the recent
past. Such simple heuristics will generate customer insights that will be
useful for guided selling and lead to increased sophistication over time.
5.
Augment the customer
engagement process. AI guided selling solutions must enrich and augment
the current customer engagement process. If an AI guided selling initiative
requires salespeople to change the customer engagement process to accommodate,
adoption will be difficult. AI guided selling solutions must fit within the
natural workflow of the sales representative.
The machine is no replacement for a
salesperson but if used cleverly, AI can drive greater effectiveness for the
sales organization. Like most AI projects, it’s not an easy goal to achieve,
but with persistence, the hype can live up to reality.
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