Acquisition project | Actuals

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Acquisition project | Actuals

Hi there, we'll take this one step at a time!

If you struggle with a blank canvas, use this boilerplate to start. Remember, this is a flexible resource—tweak it as needed. Some sections might not apply to your product and you might come up with great ideas not listed here, don't let be restricted.

This is not the only format, we would love to see you scope out a great format for your product!

Go wild and dive deep—we love well-researched documents that cover all bases with depth and understanding.

Refer to the project brief and the additional resources before you begin this project!
(Go through them at least 3 times or till the time you don’t have a mind map in your brain)

Let’s begin

Elevator Pitch

(Think of this as an introduction to your product- make it remarkable

A Snowflake-like solution for business users, designed to eliminate data chaos with data teams, saving $500K to $1M annually

Understand your Product

(Before you begin, you need to know what your product is, what are its features, what is the problem being solved by your product?)

About product:

Actuals is a combination of data engineering and business intelligence tools designed to be used by business users. It’s powered by GenAI in order to make it easy to understand and use for business users.

Features:

Low-code-no-code, One click deployment, Connected Systems (Connectors+Transformation+Business Intelligence all in one solution), GenAI (LLMs, RAGs, filtering, tuning, prompt engineering)

Problems:

Maximum Micro & small enterprises drive data in a very traditional way, i.e. using excel and google sheets and at max they use BI tools - This is not scalable in the long run.

Few small enterprises & majorly all medium enterprises are inclined towards building a data team for driving data, but it becomes an expensive affair in a shorter term for them. How? Most data solutions available in the market are built for developers, that means one requires a tech team to use such tools, i.e. expenses both on licences as well as on manpower). It also doesn't solve the chaos that occurs when different teams chase data teams for reports/ metrics every month.

Understanding Core Value Proposition

(Build your core value proposition by exact what your product does and what problem are you solving)

Core Value Proposition:

One system for all designed to solve for intact Data pipeline that reduces data leakage and pipeline breakage issues: connectors, ETL (Extract Transform Load) pipeline, ML (Machine Learning) pipeline, BI (Business Intelligence) pipeline
GenAI in Data Engineering solves for evergoing gloat of data engineering complex nature
Affordable product cost so that even micro enterprises can purchase & adapt best business practices of managing data
Customization leads to confusion, we offer standardisation in our product, i.e. high cost cutting on support/ software maintenance

Problem we are solving:

Based on the intelligence we gather in our MVP phase, we decided to build a plug-n-play product to solve for:

- Data control and privacy

- Cutting cost on support and maintenance of product, DIY & Community support

- Can be a pure product business play

- Can be used for financial, operational, any form of data

- Can create MIS dashboard, reports, charts/ graphs

Understanding the Users

(Go and speak to different users of the product and the people in the chain: households buying the product, shopkeeper selling the product, churned users, users using competitors products. In case of B2B products identify the decision makers, the influencer, blocker and the end user)

Customers’ paint points in managing data:

Micro Enterprise:

Punt Partners: Madhu Sudhan (Founder):

Our ultimate aim is to build an organisation’s performance dashboard which eventually requires us to make many metrics. We do not have data people onboard, we need the simplest form of solution that can fetch data from different sources and build metrics without having to write much coding.

Small Enterprise:

Betterhalf: Pawan Gupta (Founder):

Most of the data solutions support/ encourage customization, we are at an early scaling stage, we cannot afford to dedicate even one person for using data solutions. We want a product which can be purchased and used without having intervention from the outside world.

Medium Enterprise:

Ather Energy: Tarun Mehta (Founder), Deepak Jain (ex-CFO), Ankit Mogra (BI Head)

Collective Feedback:

Built an ecosystem of different solutions connected to translate raw data into meaningful insights. Current challenge is the scale of the solution built by using AWS RDS as data lake, metabase for fetching data from data lake, and Qlik for data visualisation. Data transformation happens either in Qlik or Metabase but it happens through scripting for which Ather has built an internal data team. The whole ecosystem comes at an annual cost of more than a million dollars (manpower, data storage & CPU cost, licences cost, and support & maintenance cost). We want a data orchestration solution around Qlik (3 years contract constraint). Talend (Data management) by Qlik can solve for scalability but will not solve for yearly cost.

Other feedback: If Actuals has to take responsibility for customization, this is not a product you are selling, it's a service business play. Also, the cost of doing that will also be high as you would require reserved staff to support your customer as & when required.

Understanding your ICP

(There are separate tables for both B2C and B2B products, put down your your ICP’s in a Table Format, use this as a reference.
This table makes it super clear for anyone to understand who your users are and what differentiates them)

B2B Table:

Criteria	ICP1	ICP2	ICP3
Name	Micro Enterprises	Small Enterprises	Medium Enterprises
Company Size	300-500	500-1000	1000-2000
Location	Metros & Tier 2 cities	Metros & Tier 2 cities	Metros only
Funding Raised	$5mn - $15mn	$15mn - $40mn	$40mn - $150mn
Industry Domain	D2C brands/ Retail, Consumer Goods	D2C brands/ Retail, Manufacturers & Automotive suppliers, Insurance	Insurance, Energy, Real Estate, Manufacturers
Stage of the company	PMF, Early Scaling	Early Scaling, Matured Scaling	Matured Scaling
Organization Structure	Horizontal or flat org structure OR Team-based org structure	Functional org structure	Hierarchical org structure
Decision Maker	Founders	CXOs	CXOs & HoDs
Decision Blocker	CXOs	HODs	Finance/ Legal/ Strategy
Frequency of use case	Weekly	Weekly to Monthly	Monthly to Quarterly
Tools Utilized in workspace	MS excel, Google sheets, Zoho suites, Tally,	Google sheets, Zoho Suites, Tally, FreshDesk Suites, Darwinbox	Salesforce, ADP honeywell, SAP, etc.
Organisational Goals	Sales, Revenue, Funding	Revenue, Expansion, Cost	Cost, Profits, IPO
Preferred Outreach Channels	Founder’s led direct reach (Network), LinkedIn reach, SaaS events driven	LinkedIn reach, Online Marketplace, Referrals, Content loop, Founders led direct reach (Network), SEO	Online Marketplace, Investor referrals, Content loop, paid ads, SEO
Decision Time	<1 month	1-2 months	2.5-3 months
GMV	$2-4mn	$4-10mn	$15-30mn
Growth of company	200-400% yearly	100-150% yearly	50-80% yearly
Motivation	Valuation, Company PR, working towards vision	Unicorn Status, Going multinational	IPO, taking conglomerate route - diversifying business, competing with establish large enterprise

We have multiple users of a product and not all of them can be our ICP for whom we make our strategies, we need to prioritize.
(use this ICP prioritization table)

Criteria	ICP1 (Priority: 2)	ICP2 (Priority: 1)	ICP3 (Priority: 3)
Adoption Curve	Medium	High	Low
Appetite to Pay	Low	High	High
Frequency of Use Case	Medium	High	High
Distribution Potential	High	Medium	Low
TAM	~$1.1 bn#customers=90k+ACV=$12K	~$1.5 bn#customers=60k+ACV=$25K	~$2.5 bn#customers=50k+ACV=$50K

Understand Market

(begin by doing a basic competitor analysis)

Factors	Qlik+Talend(Matured scaling stage)	Snowflake (Matured scaling stage)	Syncari(Early Scaling Stage)	Opendatasoft(Matured Scaling Stage)
What is the core problem being solved by them?	Qlik is mainly known for their business intelligence stack, competing with Tableau and likewise.	Snowflake primarily addresses the problem of data silos, scalability, and performance in data management	Autonomous master data management (MDM)	Fundamental of business Data Democratisation: Data is now everyone’s business. Hence the name: opendatasoft.
What are the products/features/services being offered?	Data Integration, Data lake, Data warehousing, Data Quality & governance, AI readiness	Data lake, Data warehousing, Data engineering, AI/ML applications in data management	Data Management, Data sync, Reporting & Analytics, Integrations, etc.	Data Management (Integrations, Data Visualization, etc.), User Experience (Intelligent AI search, data catalogue using open data/ publicly available data)
Who are the users?	Customers: Large EnterprisesUsers: Developers & Data Analysts	Customers: Large EnterprisesUsers: Developers	Customers: Medium & Large EnterprisesUsers: Data Analysts	Customers: Large EnterprisesUsers: Data Analysts
GTM Strategy	Selling Talend to existing customers who are using Qlik as BI tool. They mainly target large enterprises	Community driven sales; marketplaces driven sales, product integration led sales, brand driven sales. They mainly target large enterprises.	Partner driven sales, product integration led sales.	Targeting large enterprises using existing champion CRM/ ERP marketplaces
What channels do they use?	Offline events like Seminars, etc., Organic Channels, Referral Programs, Paid Ads	Partner programs (Gold/ Platinum/ Silver), Product integration, Paid Ads	Organic Channel, Content loop on SaaS product reviews website (like g2.com) by their existing customers	Partner programs, product integrations
What pricing model do they operate on?	Licences subscription with min #user: 10Implementation/ maintenance cost separate	Data: CPU usages, Data Storage size basis cloud type basisAI: $X/ 1M tokensImplementation/ maintenance cost separate	Licences subscriptionImplementation/ maintenance cost separate	Flat pricing per enterprise per year for the whole solutionImplementation/ support/ maintenance cost separate
How have they raised funding?	$3.69 bn (Thoma Bravo, CPP Investments, Ares Management, and Golub Capital)	$1.6 bn (Sequoia Capital, ICONIQ Capital, Dragoneer Investment Group and Salesforce Ventures)	$17.3 mn (Crosslink Capital, SignalFire, Dig Ventures, and Animo Ventures)	$36.3 mn (Alven Capital and Iris Capital)
Brand Positioning	Qlik mainly know for their BI tool, which they originally started with	Still known and opted for data warehousing/ data lake solutioning	Mainly known for its data automation platform that helps organisations unify, clean, and sync data. BI is still unknown and unadapted	It is particularly recognized for enabling the creation of data portals and dashboards
UX Evaluation	Mandatory launch tutorial and 3-5 mins for creating a new instance for new customer/ users. Connectors layer can be understood and operated by non-technical whereas ETL is a slightly deeper layer for non technical to understand and operate. Qlik BI is not completely UI/UX driven, complex metrics requires scripting. Source: https://www.youtube.com/watch?v=W3S3n9RM2ew	Main window and sub windows through which data can be fetched, clean, join with other tables. Snowflake is driven by SQL and mainly designed to be used by SQL developers. Snowflake also allows fetching publicly available datasets/ databases to your existing dataset or to use it as a new dataset. Data Visualization can be done right on the table using right side bar and it looks intuitive for not so complex metrics. Source: https://www.youtube.com/watch?v=7VNcQb5f9P4	Connectors layer is intuitive and design as a flow diagram for user to understand data flow from source to destination. Useful in case of many to many source and destination chart. ETL layer covers very limited use cases and it requires further implementation from Syncari to add more use cases. Metrics builder looks very old fashioned and it doesnt show the SQL being made as we build a new schema. Source: https://www.youtube.com/watch?v=Byu9p7lp7xQ&t=39s	Majorly used/ known for local and public data sets and UX support uploading csv/ xlsx/ etc files from local drive. Very few cases are covered in ETL - defining data type, sorting, filtering, etc. Source: https://www.youtube.com/watch?v=k2jqCST9nnQ
What is your product’s Right to Win?	Target Micro & Small enterprises where competitors lack presence because of their high costing	Lower risk & faster pace in introducing new features like GenAI driven data management in comparison to competitors.	Starting with small enterprises, followed by micro enterprises can let us experiment in PMF stage and make us ready to target medium and large enterprises later	Target Micro & Small enterprises where competitors lack presence because of their high costing
What can you learn from them?	Force-fitting integration between different stacks	One ecosystem of all stacks (connectors, ETL, BI) always win over multiple solutions connected to serve the same purpose	Medium enterprises are now moving towards adapting enterprise level solutions like snowflake. Targeting them means competing with establish players	Targeting large enterprises without making a credible name in the market is challenging and cost ineffective

(then try to understand the market at a macro level and evaluate the trends and tailwinds/headwinds.)

Now it’s time for some math, calculate the size of your market.

TAM = Total no. of potential customers x Average Revenue Per Customer (ARPU)
SAM = TAM x Target Market Segment (percentage of the total market)
SOM = SAM x Market Penetration/Share

Now it’s time for some math, calculate the size of your market.

TAM = Total no. of potential customers x Average Revenue Per Customer (ARPU) = $5bn+

Micro Enterprises: SAM ~$1.1 bn; #customers=90k+; ACV=$12K

Small Enterprises: SAM ~$1.5 bn; #customers=60k+; ACV=$25K

Medium Enterprises: SAM ~$2.5 bn; #customers=50k+; ACV=$50K

SAM = TAM x Target Market Segment (percentage of the total market) = (India & US) = $2bn+

Micro Enterprises: SAM ~$450 mn; #customers=37k+; ACV=$12K

Small Enterprises: SAM ~$600 mn; #customers=24k+; ACV=$25K

Medium Enterprises: SAM ~$1 bn; #customers=18k+; ACV=$50K

SOM = SAM x Market Penetration/Share = 20% of SAM in another 10 years = $400mn+

Micro Enterprises: SAM ~$90 mn; #customers=7.5k+; ACV=$12K

Small Enterprises: SAM ~$120 mn; #customers=5k+; ACV=$25K

Medium Enterprises: SAM ~$200 mn; #customers=4k+; ACV=$50K

Source: Crunchbase

Designing Acquisition Channel

(keep in mind the stage of your company before choosing your channels for acquisition.)

Stage of the company = PMF (Cheaper, Faster & Feedback driven)

Channel Name	Cost	Flexibility	Effort	Speed	Scale	Budget
Founder led sales	Low	High	Medium	High	Low	Low
Events driven sales/ feedback	Low	High	Low	High	Low	Low
Referral Program	Low	High	Medium	Low	Medium	Medium
Content Loops	Low	Low	High	Low	Low	Medium
Product Integration	High	Low	High	Low	Medium	Low

Detailing your Acquisition Channel

Founder led sales

Direct Reach:

Reaching out to the companies where founders/ co founders/ founding teammates have worked before OR reaching out to ex-colleagues/ friends/ family for the companies in their network. This is the fastest way to make trials happen for your product.

Reach via Media, i.e. LinkedIn:

Reach out to founders/ CXOs from your network and request for 30 mins of their time to bring them to see your product. Once something excites them about your product, extend a week trial to them and set up a follow up meeting with them after a week.

Events/ Community driven sales trials:

Offline/ Online Events:

Events on AI/ SaaS by Google (like Google for startups), investors (like AI startup day by IvyCamp), SaaSBhoomi community events, Salesforce events where founders get a chance to demonstrate their product to the SaaS leaders and get feedback from them.

Content Loop

(Keep it simple and get the basics right)

Step 1 → Nail down your content creator, content distributor and your channel of distribution
Step 2 → Decide which type of loop you want to build out.
Step 3 → Create a simple flow diagram to represent the content loop.

Hook: Blogs on Medium.com; newsletter on recent update in the company: posted on company website, link of the same with high level content shared on company’s linkedin page

Generator: Employees (copy writers)

Distributor: in the form of newsletter shared to customers, leads, product qualified leads (one who tried product) monthly

Hook: SaaS product review website like g2.com

Generator: Customers writing reviews after using product in free trial for 2 or more months

Distributors: Employees sharing best reviews on company’s LinkedIn page as well as on company's website with the link to go to the original source

Product Integration

(Understand, where does organic intent for your product begin?)

Step 1 → Understand does your product fit in?
Step 2 →Draw a possible flow of how the product will look like inside the integration.
Step 3 → Create a plan of multiple integrations that you could do.