Advanced analytics can drive the next wave of growth for transportation and
logistics companies
Even without perfect data, T&L companies
can generate sales growth through analytics-based insights. Here’s how it’s
done.
Transportation
and logistics (T&L) companies have
embraced advanced analytics in their operations, allowing them to run
sophisticated global networks. Unfortunately, those investments in analytics
haven’t been matched on the commercial side of the business. It’s time for that
to change if companies in the T&L sector want to drive sales performance
and growth.
Overall, the sector’s commercial analytics
capabilities lag the average performance of B2B companies, which suggests that
there is considerable potential (see exhibit). That’s primarily because
distributed sales forces, common in T&L companies, often rely on outdated
processes and don’t have accurate insight into customer preferences and growth
opportunities. Catching up shouldn’t be daunting. T&L companies already
have much of the data they need and can turn to analytics programs that are
proven to work. Based on our experience, companies in the sector that embrace
analytics can generate an additional 3 to 5 percent return on sales.
Such commercial success is not simply a
nice-to-have. T&L companies are vulnerable because their increasingly
sophisticated customer base has access to more products and services than ever
and is adept at evaluating prices. In the trucking industry, for example, new
entrants such as Uber Freight and Convoy have given customers transparency into
the baseline cost for any journey.
Other B2B industries are already marching
down the analytics-enabled path to commercial growth. T&L companies that
have turned to analytics to drive growth have successfully addressed customer
churn, improved customer leads by understanding which have the most potential,
increased cross-selling and upselling, boosted sales-rep productivity, and
tailored pricing.
But to deliver this level of commercial
excellence, sales leaders must overcome some significant organizational
barriers—not least the commercial function’s lack of access to operational
data, which is so crucial to optimizing the commercial engine.
In our experience, T&L companies need
to do three things to realize the potential of advanced analytics.
1.
Wrangle the data you already have
The first step is to recognize what useful
data already exists in the organization. It just requires some creative
thinking to get it. For example, a distributor realized that although its
customer-relationship management (CRM) system tracked customer volumes, it didn’t
track them by location. That information did exist but was
buried in the company warehouses’ order-management system, which tracked order
dates, volumes, and delivery destinations. By analyzing the warehouse data set
instead of the CRM, the company could learn how customer buying patterns
shifted when new SKUs were introduced, when prices were increased, or when
delivery frequencies changed. The newfound insights allowed them to create a
predictive churn model that allowed proactive intervention and reduced churn by
15 percent (2 percent of revenue).
Accessing operational data from within
your own company is surprisingly hard. The commercial team needs to find way to
learn about what operational data exists and how it is being used by talking to
operational leaders about what data they use, asking data analysts who work on
operational topics, or taking a step back and looking at the companywide master
data. To help generate excitement on the operations side, the commercial team
can pick one or two specific examples of data that will generate meaningful
insights and thus show the operations team the value of the data they have.
With this operational data as a starting
point, companies can then look to combine it with specific external data. This
might include potential customer locations, total revenue, industry dynamics,
etc. One distributor, struggling to determine which customers its sales force
and delivery teams should prioritize, combined its data on delivery sites with
mobile data on foot traffic to identify locations with the highest potential
spend. Combining insights like this with prioritization and rigorous outreach
has driven growth by 5 percent or more in some markets.
2.
Invest in explaining the data and keeping it simple for the sales team
Sales teams typically have neither time
nor the inclination to delve into data. Predictive models, for example, are
often ignored because they are not 100 percent accurate; managers need to
explain that even modest accuracy (predicting that 60 to 80 percent of
customers are likely to leave, for example) can drive growth, help target
customers, and improve sales reps’ compensation. Ultimately, for analytics to
succeed, the complexity must be minimized. The sales team needs simple and
effective insights and tools.
A North American logistics company had an
overall goal of raising prices by 3 percent annually, but it knew that wide
variation in growth and competitive intensity across its markets might make
this difficult. To meet the target, it used analytics to develop a detailed
“like customers” pricing scheme that provided simple rules based on each
customer’s profile: reps were expected to raise prices on a certain type of
customer while maintaining or even decreasing prices for a different type of
customer. Customers were grouped based on a host of characteristics, including
obvious ones like their size and location, but also less obvious ones like the
product shipped, the destinations they needed to reach, and when they had
volume spikes.
Reps embraced the new concept with great
enthusiasm. The new approach also made performance management easier, as reps
accepted that local market characteristics were being considered and that they
were being benchmarked against peers from similar markets with similar customers.
Similarly, a cargo airline developed a
complex micromarket strategy to categorize customers based on flight/space
availability and demand. The airline’s new model essentially recognized which
customers contributed more in challenging micromarkets and rewarded them
accordingly. The impact of the analysis was tremendous. It helped the company
identify opportunities for different negotiation strategies, raising prices for
flights on routes with high demand or asking for higher-volume commitments on lower‐demand routes. To make it easy for the
sales force, the company developed a simple performance dashboard for sales
teams to manage pricing and volume negotiations with large customers by route
on a daily basis.
Making analytics insights actionable for
sales reps also requires coaching. The underlying analytics model can be hard
to understand, but a second level of modeling (technically called a LIME or
SHAP model) often makes it much easier to explain why a customer was selected
or targeted. A transport company built a lead-generation engine that fused
recommendations from three independent models. The new system published the
characteristics and relevant context of each lead into one-page profiles that
sales reps could then use in their conversations. This information also gave
the sales manager a set of talking points or actions on which to coach the rep.
3.
Embed analytics in daily routines
Behavioral change is particularly hard in
T&L companies, where sales teams are distributed across many sites, rarely
meeting in person at a terminal or office. For these people, there is often no
formal way to build new capabilities. A distributor addressed this issue by
building a routine around insights generated by a commercial analytics engine
that identified pricing opportunity and churn risk. Reps were coached to sit
down on Friday and plan their next few weeks based on the insights (such as
prioritizing visits with customers with the biggest opportunity, or running a
consistent play, such as addressing customers who needed margin improvement).
Managers knew to ask reps how they prioritized their visits and then to ground
their weekly coaching session in the analytics insights (what to say to get the
best outcome, for example). The combined approach increased volume in its
target segment while increasing margin (by reducing volume) in less-profitable
segments, thus improving EBITDA in the business group by more than 30 percent.
Sales reps naturally want analytics they
can understand at first glance, but the insights need to be presented in such a
way that they can delve into the details when they need to understand what lies
behind the recommendations. Allowing reps to dig deeper turns a report into a
tool that enables them to develop and have confidence in their own insights. A
year after one distributor introduced analytics, the sales reps recognized how
much better they knew their customers because they had seen so much data over
the previous 12 months and were actually clamoring for more.
Another critical component of embedding
analytics is to hold people accountable for using them. That starts with
setting targets, then showing the sales team the analytics-based
recommendations on accounts, products, or actions that will help them reach the
targets. Those targets should be coupled with incentives.
The key
to getting started: Just get started
Even basic analytics can deliver value, so
companies should not hesitate because they believe they need perfect data.
After all, insights are useless if they take too long to develop. Nowhere is
this more evident than when an analytics team disappears for weeks, only to
resurface with some terribly clever analysis that neither they nor their
internal customers know what to do with. We have seen extremely complex pricing
and upsell models developed over six months, but we have also seen models
developed in as little as six weeks that spark initial tests.
Many T&L companies lack the
data-engineering capabilities to jump-start their programs and partner with
third parties who can build the early algorithms. At the other end of the
spectrum, one leading logistics company has hired more than 100 data
scientists, embedding many in the business units to learn the business and
crack its highest-value use cases. Whether companies want to do the data
science in-house or outsource it, the most important step is simply to start.
Advanced analytics can prove its value very quickly.
One T&L organization kicked off an
analytics program by bringing together senior commercial leaders and people familiar
with internal data to brainstorm what types of insights would be valuable. The
team decided it wanted to understand what was driving customers to switch to
other providers. The company then embarked on a four-week effort to clean and
organize the relevant data by looking at the order and fulfillment histories of
customers who had recently left. This was done by a relatively low-cost data
engineer so was not a major effort.
The company then started a series of
two-week sprints to build algorithms and generate insights. The commercial team
reviewed them and helped to refine the models in an early stage. It was quickly
time to test them in the field using a well-designed A/B pilot. Sales reps were
given details on which customers were at risk of leaving and, based on the
algorithm, recommendations on how to entice them to stay. For some it was a
question of price, others had concerns about service quality, and some were not
aware of the breadth of the company’s offerings and thought it could not meet all
their needs. These insights were pushed to the sales team, who were coached to
ensure they knew how to act on them. The pilot was successful, delivering a
two-percentage-point uptick in revenue and a ten-percentage-point improvement
in EBITDA.
Transportation and logistics companies
have enormous potential to deliver commercial success by investing in advanced
analytics to mine the data they already have, even if it’s not data that sits
within the sales function. Up to 5 percent return on sales is achievable for
firms that are able to make creative use of their existing data, keep insights
simple for their sales teams, and embed analytics into their daily routines.
https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/advanced-analytics-can-drive-the-next-wave-of-growth-for-transportation-and-logistics-companies?cid=other-eml-alt-mip-mck-1811&hlkid=0265d63aefdc434d8efedbf56065b0ed&hctky=1627601&hdpid=6ae488ea-9d66-4d9b-92e7-e68a895a828b
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