Sales Performance is a Data Problem, not an Effort Problem

“In God we trust. All others bring data.”  (W. Edwards Deming)

Ask a veteran salesperson to see their black book. The worn pages. The names are called for fifteen years. The dozen accounts they trust. It is a remarkable thing, and it is also the whole problem. We send good people out with dirt under their nails and a commission that punishes a slow month, then ask them to find the market with a memory and a windshield.

And we act surprised when performance swings wildly from one rep to the next. It isn’t effort. Nobody out there is loafing. We turned selling into a guessing game and called the guessing “territory management.”

Watch a sales manager actually plan the week. A little of it is the CRM. Most of it is last week’s conversations, the squeaky wheel, the account that happens to sit near Saturday’s ballgame. The plan gets built on what’s top of mind, never where the opportunity is, because no one has ever handed the manager a picture of where the opportunity is. Habit, repeated across every rep and every territory, is how a dealer quietly leaves most of its market untouched.

Here is the uncomfortable part.

The reason performance is uneven is not that some people try harder. It’s that none of them can see. The list most teams work is built from public filings that, in our analysis, match the best customer profile by less than ten percent of the time, and outweigh the smallest firms in the market. Ask a rep to win on that, and you’ve set them up to fail, then graded them on the failure.

Fix the data, and the effort finally has somewhere to go.

Now imagine the other version. By Monday morning, each rep has twenty-five accounts to cover this week, and the reason for everyone, and something to say when they walk in. The list is built not on who is called last, but on the odds that each account actually buys, with the fleet they run and the dollars at stake sitting right next to the name. Across every rep and every territory, you can see who worked on their list and who didn’t. And at quarter’s end you know not just that the number came in, but exactly where you won, and why.

The best part is what this does for the salesperson. Every good rep has said some version of the same thing: give me half a chance to prove I’m good. A defined list is that chance. It doesn’t replace their judgment or their relationships; it points them. It takes the part of the job that was luck, knowing which door to knock on, and turns it into something you can hand them and hold them to. The craft of winning the deal is still theirs. We just stop making them find the deal blindfolded.

And this is bigger than any one dealer. The manufacturer who builds the machine, the dealer who sells it and the rental company who keeps it running all answer the same customer in the end: the contractor who has to get the job done. Serve that contractor well, with the right machine at the right time, and the whole industry moves forward together.

That is what BiltReady is built to do: replace the generic prospect file with a scored list of named accounts, each carrying its total fleet value, its probability to buy, and the dollar opportunity quantified across the three ways a dealer earns: new-equipment sales, rental, and parts and service. No other source puts a number on all three. It is built by data scientists and proven against real outcomes.

If you want a fresh approach to your market, let’s talk. It’s not magic, just math.

No Opportunity Left Behind.

BiltData.ai analyzes 100M+ transactions to keep construction equipment OEMs, dealers, and rental companies in the path of growth. Different links in one chain, united by a single goal: to serve the contractor in a way that lets them succeed. BiltReady, our quantitative buyer-signal model, surfaces what UCC filings miss. Forward-looking, named-account opportunities at the firms with real spend, scored by buyer probability and a quantified dollar opportunity across new-equipment sales, rental and parts and service, with quarterly timing per account.

 

Market share is the most comforting number you can’t actually move

“In God we trust. All others bring data.”  (W. Edwards Deming)

Here’s how it works today. The share chart goes up at the quarterly review. If it ticked up, everyone relaxes. If it ticked down, someone gets a talking-to. Either way, the meeting ends without a single account named.

Market share is the number OEMs and dealers report and no one can act on. You cannot sell “share.” Worse, most turn to UCC (uniform commercial code filings, or “public liens”) to see what their competitors are selling, the one source that over-counts the smallest companies and individuals and misses most of the real buyers. Use that file to find prospects and you are working harder, not smarter. Never lean on it to track a competitor’s true activity.

Trust that comforting number and it will quietly mislead you. Share can climb while you lose ground. In one territory, the industry grew about 45% in a single year while the dealer’s share fell from 22 points to 14. And you only ever see the deals you won, never the ones you lost, so the one thing you most want to know, the share you are truly winning, is the one thing the scoreboard cannot show you.

Market share is coverage × close.

(The math is simple. Market share is the units a brand sells divided by the units the whole industry sells, measured one category of equipment at a time, such as wheel loaders.)

Coverage is the share of the right buyers you actually reach. Close is how often you win them once you do. Move either one and market share moves with it.

Two divisions of the same dealer can post nearly identical share and have opposite problems. One covers barely a third of its market but closes almost everything it quotes. The other blankets the territory and closes almost nothing. One needs breadth. The other needs depth. You cannot see either in a share figure, and the fix is worth real money.

Run it forward and it stops being abstract. A single point of share turns out to be only a few more machines a month, reachable by widening coverage in the counties you under-quote, not by hiring. The number you could never act on becomes a short list of specific moves.

Now imagine every rep opening a weekly list of the accounts you’re not yet covering, ranked, with the reason each one matters, and being measured on whether coverage widened, account by account, instead of on a lagging number they can’t touch. That is the difference between reporting the past and directing the present.

Market share is also the language the whole industry shares. The OEM building the machine, the dealer selling it and the rental company renting it are all working toward one end: serving the contractor well enough that the contractor wins, and comes back. Coverage and close are simply how each of them earns that share, one account at a time.

BiltReady makes it real. It turns market share into a number you can actually measure, won plus lost over the whole industry, with a step that finally captures the deals you used to lose in silence. For every named account it also puts a dollar figure on the prize: the total fleet value, and the opportunity in new-equipment sales, rental, and parts and service. Nothing else quantifies all three, and the numbers are built by data scientists and proven against real outcomes. Coverage becomes the leading indicator you take to leadership. The leverage is in aiming at the right buyers rather than the most familiar ones: in our analysis, the same effort pointed through BiltReady converts to a closed deal four to six times as often. And the math gets concrete. About a point of share is a few more units a quarter per division, with no new headcount.

Market share tells you what already happened. Coverage and close tell you what to do Monday morning.

No Opportunity Left Behind.

BiltData.ai analyzes 100M+ transactions to keep construction equipment OEMs, dealers and rental companies in the path of growth. Different links in one chain, united by a single goal: to serve the contractor in a way that lets them succeed. BiltReady, our quantitative buyer-signal model, surfaces what UCC filings miss. Forward-looking, named-account opportunities at the firms with real spend, scored by buyer probability and a quantified dollar opportunity across new-equipment sales, rental and parts and service, with quarterly timing per account.

Your Best Rep is Knocking on the Wrong Doors

“In God we trust. All others bring data.”  (W. Edwards Deming)

Picture your best salesman. He works on a black book, the names he has called for years, the dozen accounts he trusts, and he fills the rest of the week with instinct. Nobody is loafing. The effort is real. The problem is the map he was given.

Every dealer sells new iron off roughly the same list: the UCC financing file. It feels prudent because everyone uses it. That is exactly what makes it the most expensive habit in the building. Because every dealer pulls the same financed names, your reps spend the week elbowing rivals for the same handful of accounts, while the operators who quietly run the largest fleets, and finance the least, never surface at all.

Start with the belief we are trained in most deeply: that a customer is a customer. The data says otherwise. In the fleets we have analyzed, about one buyer in five drives roughly three-quarters of the value. The rest is a long, thin tail, and a few of those accounts actually cost you money to serve.

Now hold the financing list up against that truth, and watch it invert the market. The overwhelming majority of those filings come from the smallest firms in your territory, the ones that buy the least; four in ten don’t match a real operating business at all. The file doesn’t just miss the market. In practice, it points to your best people at the wrong end of it.

Picture the opposite. Every Monday, each rep opens a short, ranked list of named accounts most likely to buy a machine next, scored by the fleet they run, their probability to buy, and the aging competitor iron they’re due to replace. The contact is there. The reason is there. The rep walks up to the door already knowing what’s behind it.

And he’s measured on something honest: not activity nobody can verify, but whether the list got worked. That is the precise opposite of a week built on a hunch off a file every rival also holds.

None of this is really about one rep, or even one dealer. The manufacturer who builds the machine, the dealer who sells it and the rental company who put it to work are all chasing the same thing: the right iron in a contractor’s hands at the moment it helps them win the job. Point the whole chain at that, and the market share looks after itself.

That list is BiltReady. In place of the financing file, it hands each rep a scored set of named accounts, everyone carrying its total fleet value, its probability to buy, and the dollar opportunity quantified across the three ways a dealer earns: new-equipment sales, rental, and parts and service. Nothing else puts a number on all three. The scores are built by data scientists and proven against real outcomes.

A new equipment sale was never evenly distributed across the market. The standard list only made it look that way.

Stop selling to the wrong third. It’s not magic, just math.

BiltData.ai analyzes 100M+ transactions to keep construction equipment OEMs, dealers, and rental companies in the path of growth. Different links in one chain, united by a single goal: to serve the contractor in a way that lets them succeed. BiltReady, our quantitative buyer-signal model, surfaces what UCC filings miss. Forward-looking, named-account opportunities at the firms with real spend, scored by buyer probability and a quantified dollar opportunity across new-equipment sales, rental and parts and service, with quarterly timing per account.

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Why 83% of Construction Companies Drive Less Than 23% of Employment

Why AI-Driven Targeting of the Profitable 10% for 25%+ Revenue Growth

Why Market share Growth Can be in Your Pocket

Drawing from proven data-driven strategies in competitive industries, and BiltData.ai’s deep dive into over 100 million transactions across construction equipment rentals, fleet ownership, and product support—one truth stands out: success in sales is not about casting the widest net; it’s about focusing on the prospects most likely to become loyal, profitable partners. Too often, companies chase the wrong clients—those with low staying power and minimal potential for long-term value, wasting resources on segments that yield diminishing returns. Instead, the key lies in identifying and replicating your high-value customers through precise, math-based targeting. Market share gains can truly be accessible, in your pocket when equipped with the right technology, tools and team. BiltData is one such team pushing the limits of using AI to bring unparalleled value to the construction equipment industry.

Having spent years in the trenches, and supported by the best industry minds such as Ron Slee, I can tell you: it is not magic, just math. Granted, math at mega-scale. And in today’s market, where data chaos can bury your best opportunities, transforming raw data into actionable insights starts with a simple choice—be contrarian, avoid the crowd’s tactics, and zero in on what really moves the needle.

“A staggering 81% of all businesses employ 10 or fewer people, yet these small operations account for less than 10% of the total workforce.”

Consider the broader U.S. business landscape: A staggering 81% of all businesses employ 10 or fewer people, yet these small businesses account for less than 10% of the total workforce and even less of gross economic output. This disparity highlights a common trap:  pursuing volume over value. In the construction sector, the pattern is even more pronounced: 83% of construction businesses have fewer than 20 employees, collectively employing less than 23% of the industry’s workforce. These small companies, while numerous, often lack the scale for consistent, high-margin equipment rentals, purchases, or product support. Chasing them can lead to fleeting transactions with little repeat business, eroding profitability over time. Large enterprises might seem like the “big fish,” but they represent a tiny fraction—just 0.3% of businesses employ 500 or more, yet they dominate 50%+ of employment. Mid-tier players, however, offer the sweet spot: They have growth potential and decision-making agility, making them ideal for cloning strategies.

Picture this for major earthmoving OEMs like Deere, Cat, Komatsu, Case, VOLVO, or Bobcat. Imagine having a unified view of customer and prospect data across your dealer network, linked to real-time project intel that tells you what the best, and future best ‘buyers’ purchase, rent and consume in parts and service. This allows for accurate retail forecasting and production planning by spotting repeat buyers, the 10% driving 80% of revenue and an even greater share of profitability. Shared predictive buyer intelligence aligns incentives, hyper-focusing on specific names for joint strategies. The roadmap: start with AI-driven predictive tools to identify these buyers, then collaborate on campaigns to grow each, name by name. Benefits include cutting inventory by 20-30% (saving millions in holding costs), gaining 10-15% market share through targeted pushes, and bolstering dealer support—potentially unlocking $1+ billion in industry-wide efficiency by smoothing demand forecasts.

For dealers, visualize a day where reps skip 5+ manual steps in tabulating data, use natural language chat to effortlessly be served prospects with spend details, scoring, contacts, maps, routes, and specs in a few clicks—tied to active projects. The guidepost: integrate this into daily workflows via chat-based queries, turning data work into quick action. BiltData.ai will do this for you. This can boost participation rates by 5X+, shortening sales cycles by 30-50% and adding $1M to $5M of high margin revenue per territory manager through faster closes and higher win rates.

When OEMs and dealers team up, envision a shared ecosystem where data flows seamlessly for hyper-focus on targets. Dealers handle efficiency on the ground, while OEMs refine forecasting—all through aligned goals like joint campaigns. The path: use unified platforms to merge insights, then execute targeted outreach. Value lies in amplified ROI, with complete visibility reducing supply chain waste by 25-40%—translating to billions saved industry-wide in excess inventory and misallocated capital.

These approaches complement existing systems like CRM and marketing automation by filling data gaps for one-to-one sales and marketing. For example, flag aged competitor fleets (5-year Cat loaders for Deere upgrades) or tailor pitches on efficiency versus owned equipment. Suggest rentals for project gaps or drive parts/service by age (10-year assets). The roadmap: trigger personalized outreach from CRM, starting with high score leads. For OEMs, this unifies datasets to support dealers; for salespeople, it means 7x faster action, cutting research time and boosting close rates by 20-30%.

Drawing inspiration from “AI-Driven Value Management” by Craig LeGrande and Venkat Lakshminarayanan, AI links OEMs, dealers, rental companies, and customers to boost margins while cutting costs. By automating analysis, BiltData answers “what value can I get, will I get it, did I get it?”  In construction equipment, this means transforming raw data into actionable insights, making complex data simple so teams find and win buyers quicker.

Real-world results are embedded in deep market and customer insights such as:

  • The Philadelphia PA market, ranked #8 for 2030 spending at $47.7 billion, shows high-rises and infrastructure driving demand, with 200 MW data centers fueling tech growth.
  • Merlo America’s Geo-Forecast highlights California and Texas at $162.2 billion and $95.5 billion of 2030 construction .
  • One major earthmoving dealer solved dirty data, enabling reps to act 7x faster, boosting participation and adding millions in revenue.

In essence, it’s not about chasing every lead—it’s about selecting those with true potential. BiltData.ai transforms this complexity into clarity, delivering streamlined, multi-dimensional insights for rapid, confident decisions. We stand behind our outcomes: achieve the promised results or receive a full refund.

At BiltData.ai, we’ve empowered construction equipment OEMs, dealers, and rental companies to build this culture, enhancing decision-making through predictive buyer intelligence, actionable dashboards, and easy chat-driven tools that tie data to outcomes. Using a combination of data science and advanced technologies, BiltData reveals the most profitable sectors, the most promising prospects, and untapped territories. The era of prolonged data collection and analysis cycles is over. BiltData transforms complexity into clarity—delivering streamlined, multi-dimensional insights that empower rapid, confident decision-making. It’s not magic—just math.

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How Construction Equipment Dealers Can Succeed (Or Fail) Faster

Our new guest writers, Jordan Arsenault and Nick Mavrick bring big data to their first blog post for Learning Without Scars, “How Construction Equipment Dealers Can Succeed (Or Fail) Faster.” Don’t dig for data. Let data dig for you.

Big trouble: that’s how we are ending 2024, most economists agree. Inflation, investment bubbles, over-the-top government spending, technology disruption, dealer margin and market share compression and the exceptional market power of two major rental companies. How can your dealership thrive with more instability ahead? 

One may turn to Warren Buffet’s counsel:  “I don’t look to jump over seven-foot bars. I look around for one-foot bars that I can step over.”

In your construction equipment dealership, would you allocate capital differently if you knew how much revenue and profits were created by the top 10, or 20 percent, of your customers? What if you learned that 5% of your customers generated over 70% of revenue & profits? Or that your top customers’ life-time value was 5X or 10X larger than that of your average customer?

Consider the following construction equipment cases:

  • Major construction equipment service company:  23% of customers, 87% of revenue, each customer generates $43k annually, 5X greater than the next customer group.
  • Major equipment rental company:  3% of customers generate 62% of revenue and profits – only 15 customers per store, that each generate $250k annually 4X greater than the next customer group.
  • Major construction equipment dealer:  11% of customers, 83% of revenue and profits.

 

None of the above had been using the proportionality of their data for 80/20 capital allocation, to manage their salesforce, or in short – replicate what they are doing well, to do more of it – faster. They kept going by raising prices, cutting employee expenses, and taking incremental actions while remaining perilously close to a vicious doom cycle. Why?

  • While successful relative to their peers, they shared attributes that imperiled ability to replicate their successes by leveraging data:
  • Transaction driven, not data driven  day-to-day busyness, obfuscated strategic actions, for tactics.
  • Siloed data, and disparate systems:  DBS (dealer business system), CRM, Business Intelligence, data warehouses, ERP and more. They lacked a unified dataset.
  •  Dirty data:  garbled customer data was inconsistent by account; making it difficult to distinguish between decision-makers and influencers.
  • Tactically use of prospect databases and rarely used industry intelligence:  not linked to internal data.
  • Business Intelligence:  relegated to answering obscure ‘jeopardy’ like questions for their OEMs or ownership.
  • Territory management:  based on geography, rather than market opportunity goals. This resulted in an inability to rank salesperson performance, or more importantly evaluate share-of-wallet penetrations with high value customers.

 

For the solution, one may turn to Charlie Munger for advice:  “There is an old two-part rule that often works wonders in business, science, and elsewhere: take a simple, basic idea and take it very seriously.”

To solve data problems, BiltData.ai has constructed a Continuous Insights portal to transform a dealer’s data to quickly identify patterns and establish proportionality – which are referred to as the 80/20 rule. 

  • Data is cleansed, structured, and enriched to provide a unified hierarchy and dataset for strategic use.
  • Quick-decision dashboards for customers and prospects drive action focused on Best, Future Best and more, resulting in laser-like focus to gain and secure profitable business, faster.
  • Geographic dashboards address sales coverage deficiencies that were not previously visible, and rank sales performance. 
  • Smart leads help build market share faster by enabling multi-dimensional prospect insights, and visual filtering. This saves time by moving from cumbersome spreadsheets to one unified dataset, where action can be taken in 2-3 clicks.

The results: construction equipment companies can move quicker to take the most ‘profitable’ share, grow best customers, find the best prospects, grow share of wallet by upselling, launch new markets profitably and adopt new sales and pricing models

“In God we trust; all others bring data.” -W.  Edwards Deming

Relentlessly pursue data for your continued success!

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Jordan Arsenault is Board Chair of BiltData.ai. Jordan is the Board Chair of BiltData.ai. With an extensive career in sales management, Jordan is an expert in using data to accelerate revenue and ROI. She currently serves as Chief Strategy Officer for Southland Resources, a leading raw material energy producer.

Nick Mavrick is the CEO of Biltdata.ai. With decades of B2B data science expertise, BiltData.ai provides a tailored industry solution. We enable the construction equipment industry to get going with X-Ray vision for customer and prospect data. Nick is an expert in CRM and data mining, as the cornerstone to segmented marketing, strategy, operations and driving ROI. He has vast experience in supporting B2B sales teams including VOLVO Rents, NationsRent, multi-brand dealers and more.