Unlocking the
power of data in sales
Analytics
plays an increasingly important role in B2B sales—and high-performing sales
organizations take it to a new level to differentiate themselves from the
also-rans.
You don’t have to search too
hard to find breathless paragraphs on the power of analytics. And there are
plenty of examples in the sales world where analytics is delivering significant
improvements in growth, efficiency, and effectiveness.
In our survey of more than 1,000 sales organizations
around the world, we found that 53 percent of those that are “high performing”
rate themselves as effective users of analytics.
Yet for all the tangible benefits, analytics is still a
bit of a sideshow when it comes to sales. The same survey shows that most sales
organizations today (57 percent) do not view themselves as effective users of
advanced analytics. Many companies struggle to benefit from even basic
analytics, while some have yet to even dip their toes in the data lake at all.
Well-designed analytics programs deliver significant
top-line and margin growth by guiding sales teams to better decisions. But that
only happens when companies can do two things well: focus on areas where
analytics can create the most value, and implement wisely.
Focus: Four of the
biggest sources of value
Forward-thinking companies are using the growth of data
analytics and artificial intelligence to expand the frontier of value creation
for B2B sales and are generating remarkable results in lead generation, people management,
cross-selling, and pricing.
1. Radically improve lead generation. Analytics
is well-suited to improving the accuracy of lead generation and automating
presales processes as companies use rich data sets to identify the right
customer at the right time.
Many companies already use historical market information
to develop a detailed view of each area’s sales prospects. Some companies are
pushing this further by introducing lead-scoring algorithms based on detailed
and granular data sets on each of their prospects. Internal data sources on the
customer’s previous history are combined with rich external data such as news
reports or social media to generate a “360 degree” view of the customer. These
algorithms can then predict which factors truly matter in lead conversion and
guide sales strategy accordingly. One IT services company used such big-data
analytics to predict which leads were most likely to close—and found that
established companies were better prospects than the start-ups it had been focusing
on. Focusing its attention on established companies raised its overall
lead-conversion rate by 30 percent.
As these greater predictive insights are combined with
intelligent automation, companies are seeing a leap in their ability to
identify opportunities and convert them. Several companies are experimenting
with AI-enabled agents that use predictive analytics and natural-language
processing to automate early lead-generation activities such as handling basic
customer questions and automating initial presales questions.
Aside from being much more efficient than traditional
approaches, these algorithms can identify the most promising prospects and
pinpoint the most opportune time to target them.
2. Better match people to deals. Similar
to the way analytics revolutionized baseball by revealing the true factors
underlying wins, sales forces are using analytics to understand what drives
sales success and to inform coverage, hiring, and training.
Traditional sales planning has relied on account
segmentation, often determined more by historical local knowledge than
up-to-date facts. The result is that, over time, sales models become less
effective and globally inconsistent, while resources are poorly allocated to
accounts that require different types of sales strategies (e.g., grow versus
retain).
However, when sales-operations teams introduce basic
analytics to sales planning, resource allocation quickly becomes far more
effective. A high-tech company used a granular account and product-level
approach to realign its US coverage model. Sales productivity rose 5 to 10
percent, and the sales staff cut its planning time by two-thirds.
Analytics is also revolutionizing our understanding of
sales talent and field behavior. In the quest for the highest-performing
salespeople, organizations are combining sales, customer, and HR data to
understand the intrinsic factors driving account success. These analytics help
companies identify the best salespeople and allocate them to their most
important accounts. Analysis can also reveal the statistically important traits
of high-performing salespeople, which improves both hiring and people
development.
Taking it a step further, some companies are integrating
email, calendar, and CRM interaction data to identify which actions in the field
correlate with success, particularly for technical sellers whose value is
harder to assess. One organization used the data-science firm QuantumBlack1to
discover which behaviors of presales experts correlated with deal wins and
productivity. Based on those findings, it was able to train these presales
experts accordingly and deploy them to maximize their value. Combined with
predictive pipeline management, this reduced the cost of sales by 6 percent and
boosted revenue by 2 percent.
3. Maximize customer lifetime value. Companies
that have complex product portfolios can find it tough to match solutions to
specific customer needs. Salespeople rely on simple decision rules but this
still requires time-consuming interactions and often leads to missed
opportunities to sell related items.
Many B2B companies are implementing next-product-to-buy
algorithms that draw on data about what similar customers have bought. A
logistics company mined such historical ordering patterns to identify
cross-sell opportunities within its customer base and then built tailored
microcampaigns around those opportunities. Simply by identifying underserved
customers, the company boosted revenues fivefold for its pilot products.
The approach also helps retain customers. Engaging
customers at risk of leaving for a competitor requires recognizing the signs of
customer discontent well before they take action. These types of problems are
perfectly suited to the pattern-recognition skills of machine-learning
algorithms. The marketing-analytics team at a global chemicals company, for
instance, wanted to reduce its SME customer churn. The team built a predictive
model based on more than 30 variables and identified ten key factors that
pushed customers away. It also realized with a shock that its most important 15
percent of customers were actually three times more likely to purchase elsewhere
than other customers were. Another key finding was that the more products a
customer had, the less likely they were to leave. Cross-selling mattered and
was a stronger driver of customer loyalty than price changes. Each regional
sales manager swiftly found a list of at‐risk
customers on his or her desk with guidance on how to engage each one to ensure
they stayed loyal. Armed with these insights, the company reduced churn by 25
percent.
4. Get the right price. In
the opaque world of B2B price negotiations, deal analytics can provide price
transparency and allow sellers to make complex trade-offs during negotiations.
Traditionally, B2B sellers have relied heavily on experience to guide their
pricing decisions. But purchasing teams got smart and started deploying their
own sophisticated pricing tools, which put sales teams on the back foot.
Dynamic deal scoring relevels the playing field by
placing relevant deal information in the hands of sales reps during the
negotiation. Using decision-tree analytics, reps can identify similar purchases
and comparable deal information to guide selling. Customers with similar
pricing behavior are clustered based on factors such as industry vertical, past
purchase behavior, or size. One software company was able to increase return on
sales by more than 20 percent by providing pricing information based on
statistically similar deals to the field.
A second challenge is setting the price of new products
or solutions, particularly when there is no comparable product on the market or
market conditions shift rapidly. Companies are implementing dynamic-pricing
engines that integrate real-time competitive and market data with sales
strategies to generate optimal quotes. One online media company used dynamic
pricing to generate real-time quotes for classified space and was able to
generate 5 percent more revenue. By embedding the analytics within a
test-and-learn approach, it continued to improve and reap the benefits of
higher pricing, greater volume, and increased customer satisfaction.
Similarly, a software firm tested more than 20 different
combinations of price and value propositions, and found, surprisingly, that to
maximize revenue it needed to raise prices. Although this move cut the number
of potential sales by 10 percent, it grew the average size of each sale by 25
percent, leading to an overall increase in revenue.
Implementation:
Capturing the benefits of analytics at scale
Implementing an effective analytics program is
notoriously tricky. While some companies have struggled with execution, others
have been deterred from even getting started because of their infrastructure or
a lack of the right talent. At the same time, overworked sales leaders can have
difficulty evaluating and prioritizing various analytics initiatives when
confronted with an array of complex analytics options.
We have found that companies are most successful when
they focus on extracting the full value from a limited number of use cases
rather than trying to implement a broad-based analytics transformation.
Successfully identifying the best use cases requires analysis of both financial
impact and feasibility. It’s important, however, not to get caught up in
endless rounds of analysis. Quick and dirty analysis will often surface the
best options to start with, though additional work may be necessary to evaluate
trickier issues like scale and security.
In our experience, successful companies take five
specific actions to overcome the most significant common obstacles.
First, they recognize that perfect data do not exist.
Yet, by cleverly implementing machine-learning approaches and complementing
internal data with external sources, leading companies have been able to
extract valuable insights even from poor data. Over time, data quality improves
as positive early results justify greater investment in data infrastructure and
quality.
Second, they build data-analytics talent, but they don’t
forget the importance of field insights in the analytics engine. This means
hiring people with advanced skills in statistics and machine learning but
complementing them with experienced sales-analytics experts who can translate
the insights into actions for the field.
Third, they use low-cost solutions to get started. Many
leading solutions are relatively inexpensive and ready to deploy from the
cloud. Further investment may be needed in the future, especially in data
infrastructure, but like greater investment in data quality, it can be done
once the business value of the analytics is clear.
Fourth, they embed analytics within defined sales
workflows to ensure insights are available at the time they are most valuable,
e.g., integrating deal-scoring algorithms into sales tools and related
processes, such as deal approvals, so salespeople can use that information
during negotiations. However, very often this means integration into legacy CRM
or marketing systems. Too often this integration is considered too late or not
at all, which either means the analytics delivers a one-time benefit only,
or—worse—the insights never make it the last mile to the front line.
Finally, if insights are to drive action, they must be
accompanied by change management in the form of clear communication,
incentives, training, and performance management, or salespeople will just
ignore them.
Once
slow-moving and driven by intuition, data and new analytical techniques have
introduced greater rigor, efficiency, and insight. In many industries, it is
the adoption of advanced analytics that has begun to differentiate the winners
from the rest.
By
Charles Atkins, Maria Valdivieso de Uster, Mitra Mahdavian, and Lareina Yee
http://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/unlocking-the-power-of-data-in-sales?cid=analytics-alt-mip-mck-oth-1612
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