# Case: forty companies, who is our ideal customer? > Read Klasna for Teams' won and lost deals since 2024, rank segments by profit after service cost, and write the ideal customer profile, disqualifiers and bets for 2027. - Course: [B2B Marketing and Sales: Win Your First Company Customers](https://productovi.com/courses/b2b-marketing-and-sales.md) - Module: Whom to sell to - Lesson 8/32, ~38 min - Source: https://productovi.com/courses/b2b-marketing-and-sales/forty-companies-case ## The obvious answer **Alex Tutor:** You're still at Klasna for Teams, and this module gave you a profile, a score and tiers. Now the real test: every company Teams has talked to since 2024. **Marta:** Forty companies, first contact from January 2024 to June 2026. Leo says the answer is obvious: retail brought the most money. Check it before we plan 2027. **Sofia:** Then I start with revenue, since that's his claim? **Alex Tutor:** Yes, and then go further: revenue, contribution, service cost, profit. Won deals only, per month, while they were clients. Lost deals tell you something else: why. **Dataset (CSV):** [Klasna for Teams: 40 companies, 2024–2026](https://productovi.com/datasets/b2b-marketing-and-sales/forty-companies-case/klasna-teams-deals-2024-2026.csv) Every company Teams talked to with first contact from January 2024 to June 2026, as of 30 September 2026. For won deals, lessons and price are actual; for the rest, they are what was proposed and quoted. Money in UAH. - `company_id`: Company id - `industry`: Industry - `employees`: Number of employees - `english_staff`: Employees who need English at work - `english_use`: Why: client_work (foreign clients), partners_docs (foreign partners and paperwork), perk (a staff benefit) - `first_contact`: Month of the first conversation - `outcome`: won, lost, or no_decision (the deal faded) - `loss_reason`: Why it was lost or faded (empty if won) - `sales_cycle_days`: Days from first conversation to the decision - `lessons_per_month`: Lessons a month: actual if won, proposed otherwise - `price_per_lesson_uah`: Price per lesson, after any discount - `tutor_per_lesson_uah`: Tutor cost per lesson - `video_ai_per_lesson_uah`: Video and AI cost per lesson - `service_per_lesson_uah`: Won only: coordinator time and paid no-shows per lesson - `active_now`: Won only: still a client on 30 September 2026 **Alex Tutor:** Monthly revenue of a won deal is lessons a month × price. Add it up by industry, then compare retail with the total. > **Exercise:** What share of the won deals' total monthly revenue came from **retail**? In %, to two decimals. ## Then the costs **Sofia:** Half the revenue. So Leo is right? **Marta:** About revenue, yes. Now take off tutor 430 and video and AI 31 per lesson. Retail paid 650 to 700 after volume discounts, everyone else 780 or 800. **Alex Tutor:** Even then retail leads: 78,530 a month of contribution against 60,420 for IT services. That's why the trap is easy to fall into. Now take off the service cost per lesson. **Sofia:** Retail service runs 170 to 210 per lesson. IT is 30 to 45. > **Exercise:** What profit a month, after tutor, video and AI and service cost, did the three **retail** clients leave together? In UAH. **Sofia:** Seven thousand a month out of a quarter of a million in revenue. **Marta:** And one of them, the chain at 650 a lesson, lost us money every single month. **Alex Tutor:** Now do it for every segment with wins, and rank them by profit a month after service cost. **Marta:** Professional services and the design studios are small. Don't let size bias you, either way. > **Exercise:** Rank the four segments with won deals by **profit a month after service cost**, from highest to lowest. ## Why deals were lost **Alex Tutor:** IT services keep 53,770 a month, 298.72 a lesson. Retail brought 51.41% of the revenue and 7.66% of the profit: 19.54 a lesson. **Alex Tutor:** Put those two retail shares side by side yourself. > **Exercise:** The twelve won deals leave 94,370 UAH of profit a month after service cost; the three retail clients leave 7,230. What share of the profit comes from **retail**? In %, to two decimals. **Sofia:** And none of the retail clients is still with us. All three bought English as a perk. **Alex Tutor:** The other criteria agree. IT services and design studios won half their deals, retail 37.5%. The median retail win took 120 days; IT about 45, studios 26. **Alex Tutor:** So do the other criteria agree with the profit picture? > **Exercise:** Which statement about the other criteria (win rate, speed, staying) does Teams' deal history support? **Marta:** Logistics: four deals, no wins. Veloria is logistics. Do we stop? **Alex Tutor:** Read why: shift schedules twice, no budget owner once, an incumbent renewed once. Screen for those reasons; don't ban the industry on four deals. **Alex Tutor:** Check Marta's worry about Veloria against that. > **Exercise:** **True or false:** Teams lost all four of its logistics deals (to shift schedules twice, no budget owner once, an incumbent renewed once), so logistics should be a disqualifier and Veloria should be dropped. **Sofia:** And manufacturing and hotels? **Alex Tutor:** Manufacturing: perk cut twice, gone silent twice, shifts once. Hospitality: staff turnover all three times. > **Exercise:** Which disqualifiers does the deal history support? Select all that apply. ## Writing it up **Marta:** Write it up for me: the profile, the disqualifiers, the bets, and what we tell Leo. **Sofia:** Do I drop retail entirely? **Alex Tutor:** Drop retail as a perk for store staff. A retail head office with a real work need, like Orchid's buyers, is scored on its own facts, at the list price. **Alex Tutor:** And say how sure you are. Twelve wins is thin: mark the confidence of each line and set a review date. **Alex Tutor:** Before you write the logistics line, settle retail. > **Exercise:** Writing up the 2027 profile, what should happen to retail? > **Exercise:** Write the logistics line for the ICP page (2–3 sentences): is logistics in the profile, a bet, or out? Back it with the data and say what would change your view. **Marta:** Leo will read the memo right before the planning call. **Alex Tutor:** Then lead with the decision, then the numbers that carry it, then what you don't know yet. **Sofia:** Do I show revenue at all, or only profit? **Alex Tutor:** Both, side by side. Hiding revenue looks like hiding something. Showing both is what makes the case. > **Exercise:** Write Marta a memo: who is Klasna for Teams' ideal customer for 2027? > **Key ideas** > > - Check a «the answer is obvious» claim against the rows, one cost layer at a time: revenue, contribution, service cost, profit. > - The biggest segment by revenue can be the least profitable once service cost is counted: here retail was 51.41% of revenue and 7.66% of profit. > - Build the profile from clients who won, stayed and paid off; read the lost deals for reasons, not industry labels. > - Disqualifiers need evidence from the deals; a whole industry or small size is rarely one. > - Name bets outside the profile with the conditions that would bring them in. > - Show revenue and profit side by side, and say how sure you are. **Alex Tutor:** You now know whom to chase. The next module is about the deals themselves: a pipeline whose stages mean something. - Previous lesson: [Account tiers: one-to-one, one-to-few, one-to-many](https://productovi.com/courses/b2b-marketing-and-sales/account-tiers.md) - Next lesson: [Pipeline stages that mean something](https://productovi.com/courses/b2b-marketing-and-sales/stages-that-mean-something.md) --- Productovi Learning. Free, no sign-up needed to read.