Hello, this is Matsubara, CRM Evangelist.

"Why does that salesperson have such a high closing rate?"

Many companies respond to this question with answers like, "He has talent," "His experience is different," or "He has good chemistry with the customers."

But these are allAn explanation that cannot be reproducedThat's right. Talent, experience, and compatibility—all of these disappear from the company the moment that person leaves.

Data-driven sales is about transforming this "personal intuition" into an "organizational system." Instead of relying on intuition and experience, it involves accumulating and analyzing customer data, sales negotiation data, and behavioral data.A judgment based on scientific evidence.This is a method to improve the quality of sales.

Many people may think that "data-driven approaches are only for large corporations." However, with the spread of cloud services, sophisticated systems that were previously only available to large corporations can now be easily used by small and medium-sized enterprises. EMOROCO CRM Lite, starting at just 1,500 yen per month, is your entry point.

The Limitations of "KKD Management"—The Risks of Relying on Intuition, Experience, and Guts

In the sales field of small and medium-sized enterprises in Japan, there is a term called "KKD." It is an acronym for **Kan (intuition), Keiken (experience), Dokyo (courage)**.

KKD (Knowledge, Experience, and Communication) is by no means a bad thing. The ability to read the customer's mood, judgment gained from years of industry experience, and the courage to make bold proposals without hesitation—these are all things that undoubtedly work in the field of sales.

However, sales organizations that rely solely on KKD (Knowledge, Experience, and Knowledge) have structural problems.

Problem ① Does not scale

KKD (Knowledge, Experience, Knowledge, and Determination) resides in specific individuals. If you have 10 veteran salespeople, there will be 10 different KKDs, and these cannot be shared, transferred, or expanded. The success experience of one outstanding individual cannot be spread throughout the entire organization.

Problem ② Cannot be transferred

"Why did that customer close the deal?" "Why did that proposal resonate?" — Salespeople who operate on KKD (Know-How-To) don't articulate their processes. When the person in charge leaves the company, the know-how for success disappears along with it.

Problem 3: Cannot be improved

When asked, "Why is our closing rate low this month?", KKD salespeople can only answer, "The economy is bad," or "The timing was bad." Because the cause is not visualized in the data, they don't know what needs to be improved.

Problem 4: Managers are unable to grasp the actual situation.

When I ask for monthly reports, all I get back are phrases like, "I think we can do it," or "Please give us a little more time." Because the level of interest in a deal, the likelihood of closing it, and the risks aren't visible in the numbers, managers are left to wait for the results without being able to take any action.

How data-driven sales are transforming five decision-making processes.

As data accumulates, situations that were previously judged based on "intuition" will shift to judgments based on "evidence." Here are five specific examples of situations where this will change.

Scenario 1: Deciding which customer to prioritize.

KKD Sales: The person in charge makes decisions based on their intuition, such as "This person seems promising" or "Now isn't the right time for that company." The reasoning behind these decisions isn't explicitly stated.

Data-driven sales: Past sales data reveals patterns in which customers with certain characteristics tend to have a higher conversion rate. Variables such as negotiation phase, contact frequency, industry, company size, and decision-making speed allow for objective prioritization.

Scene 2: Deciding when to make a proposal.

KKD Sales: Acting on the feeling of "Is it okay to reach out now?" can lead to being too late and missing out after the candidate has decided on another company, or being too early and causing annoyance.

Data-driven sales: Based on the number of days since the last contact, changes in customer interest, and purchasing cycle data by industry, the system automatically visualizes "the best time to contact customers on this list this week."

Scene 3: Analysis of "Why the deal was lost"

KKD Sales: The conversation ends with comments like, "We weren't a good match," or "The price didn't work out." This perpetuates a cycle of repeating the same lost deals.

Data-driven sales: By accumulating and aggregating data on lost deals, including "reasons for loss," "competitor information," "negotiation phase," "person in charge," and "industry," patterns emerge, such as "often the problem is the timing of the proposal, not the price" and "in this industry, deals are easily lost if you can't access the decision-maker."

Scene 4: Judging "What will the landing forecast be for this month?"

KKD Sales: At sales meetings, each person reports verbally, "I think we can probably close X deals." The basis for this is intuition. There's no way to verify if they're wrong.

Data-driven sales: Because the current project's phase, probability, value, and planned closing date are visualized, it's possible to generate objective projected results such as, "This month's projected order value is ¥XX million, of which ¥XX million has an 80% or higher probability of being awarded."

Scene 5: Deciding which sales activities to invest in.

KKD Sales: Strategies like "Let's participate in trade shows" or "Let's increase telemarketing calls" are chosen based on intuition, without any evaluation of their effectiveness.

Data-driven sales: By accumulating data such as "the closing rate via referrals is three times higher than via trade shows" and "the closing rate for deals involving two or more visits is four times higher than those involving only one visit," it becomes possible to numerically determine which activities should be prioritized for time and budget allocation.

"First, gather data"—the reason why you don't need to overthink it.

Many people think that "data-driven" sales require complex analytical tools and statistical knowledge. However, the first step in data-driven sales for small and medium-sized enterprises is simple.

"First, collect the data." Analysis can come later.

Improving ROI through data analysis requires statistical knowledge, so the first step is to start with data aggregation and extraction—this is advice that is repeatedly emphasized in the field of CRM and data-driven management.

The biggest reason why small and medium-sized enterprises (SMEs) cannot become data-driven is not because they "don't know how to analyze data," but because **the data simply doesn't exist**.

Customer information is in the salesperson's notebook, negotiation results are scattered across Excel spreadsheets, and no one is recording the reasons for lost deals—in this situation, no matter how much you try to analyze, you won't have any material.

The initial goal when starting to use EMOROCO CRM Lite is not "analysis," but "accumulating data." The accumulated data will eventually generate "insights" and become the "basis for decision-making."

Implementing "Data-Driven Sales" with EMOROCO CRM Lite

Step 4: First, create a culture of "recording"—four essential data points

Initially, data-driven sales require only four types of data.

【必ず記録する4つのデータ】

1. 商談フェーズ
   （初回接触 / 提案中 / 見積済み / クロージング / 成約 / 失注）

2. 失注理由（選択式）
   （価格 / タイミング / 競合 / 決裁者にアクセスできず / 
    ニーズ不一致 / 連絡が途絶えた / その他）

3. 成約・失注した顧客の属性
   （業種 / 規模 / 最初の接触経路 / 担当営業）

4. 接触履歴
   （いつ / 誰が / 何をしたか / 顧客の反応）

With this much data accumulated, after three months you'll discover things like, "40% of our lost deals are due to timing issues, not price," and "The closing rate for deals through referrals is 2.5 times higher than for new business development."

Step 2: Use the dashboard to understand your current status in 5 minutes every morning.

In the EMOROCO CRM Lite dashboard function, configure the following views:

Dashboard for executives and managers:

【今月の営業状況】
・フェーズ別案件数と予測金額（棒グラフ）
・担当者別の成約数・失注数（比較グラフ）
・今月クローズ予定の案件リスト
・「2週間以上動きがない案件」アラートリスト

【トレンド分析】
・先月比の商談数・成約率の変化
・失注理由の割合（円グラフ）
・接触経路別の成約率比較

By simply glancing at this dashboard for 5 minutes each morning, you can see at a glance "issues that need intervention this week," "the target outcome for this month," and "team challenges." You no longer need to wait for monthly reports.

Step 3: Discover "closing patterns" and turn them into organizational know-how.

Once 3 to 6 months of data has been accumulated, we compare the deals that were closed with those that were lost.

Questions to analyze:

Q1: 成約した顧客と失注した顧客、業種・規模に違いはあるか？
Q2: 成約した案件の初回接触から成約までの平均日数は？
Q3: 接触回数が多いほど成約率は上がるか？
Q4: 提案から見積り提出まで3日以内の案件と1週間以上の案件、成約率に差はあるか？
Q5: どのチャネル（紹介・展示会・HP・テレアポ）の成約率が最も高いか？

The answers to these questions emerge from the data.

For example, if data shows that "the closing rate for deals through referrals is 60%, but for deals through trade shows it's only 15%," then you can decide to "allocate the costs of exhibiting at trade shows to initiatives that promote referrals." This is data-driven decision-making.

Step 4: Make the "success pattern" the standard for the sales team.

Patterns discovered from individual success stories can be implemented as team standards.

Implementation example:

発見：「提案から3日以内に見積りを送った案件の成約率は55%、
      1週間以上かかった案件は18%」

実装：ワークフロー自動化で「提案完了」ステータスに変更したら
     「3日以内に見積り送付」タスクを自動生成

→ 全担当者が同じ行動を取るようになり、チーム全体の成約率が改善

発見：「初回接触から2週間以内に2回以上接触した案件の成約率は
      1回のみの3.2倍」

実装：「初回接触」タスク完了後に「7日後にフォロー」タスクを
     自動生成するワークフローを設定

→ 全担当者がフォローを確実に実行するようになる

This is,Transforming "the actions of one person with good judgment" into "organizational systems"It's a process.

Step 5: Implement the improvement cycle through regular "data review meetings".

We will hold a 30-minute meeting once a month to "discuss the data."

Data review meeting agenda:

① 今月の成約率・失注率の変化を確認（5分）
   → 先月と何が変わったか

② 失注理由のTOP3を確認（5分）
   → 「価格」が増えているなら提案方法の見直し
   → 「タイミング」が増えているならフォローサイクルの見直し

③ 「うまくいった案件」を1件深掘り（10分）
   → 何が成約につながったか、次に再現できるか

④ 「失注した案件」を1件深掘り（10分）
   → 何が失注につながったか、どこで変えられたか

By going through this cycle, data becomes "material for improvement" rather than "a record for the sake of recording."

I don't dismiss "intuition and experience"—data will be their "wings."

Let me tell you one important thing here.

Data-driven sales doesn't mean "abandoning intuition and experience." The ability to read customer emotions and the sense of timing to act based on years of industry experience—these are essential sales skills that cannot be replaced by data.

The role that data plays is to replace that "intuition and experience".To transform it into a form that is more accurate, more reproducible, and usable by more people..

When you analyze the things that talented salespeople do "intuitively" using data, reproducible patterns emerge. By incorporating these patterns into the organization's systems, other employees can achieve the same results.

Transforming intuition into knowledge, and experience into a system—that's the essence of data-driven sales.

EMOROCO CRM Lite is a tool that lets you start that process for just ¥1,500 per month, no coding required. You don't need perfect data analysis from the start. Just begin by recording, accumulating, and looking at the data. After three months, you'll start to see patterns you couldn't see before.

Summary – Three Phases of Transitioning to Data-Driven Sales

Phase 1 (Months 1-3): Record

• Develop the habit of entering the negotiation phase, reason for loss, customer attributes, and contact history.
• Check the "Current Status" on the dashboard every morning.

Phase 2 (Months 4-6): Awareness

• From the accumulated data, patterns in the reasons for lost deals and common characteristics of customers who successfully closed deals begin to emerge.
• We use monthly data review meetings to drive the team's learning cycle.

Phase 3 (Month 7 onwards): Improvement

• Implement the discovered patterns into "standard organizational practices" through workflow automation.
• By replicating successful patterns, the overall closing rate of the team increases.

From sales that rely on KKD (Knowledge, Experience, and Drawbacks) to sales backed by data—this transition begins not with "advanced analytical tools," but with "the habit of daily record-keeping."

First, start your free trial of EMOROCO CRM Lite today and try entering your first sales opportunity data.
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