Brand&Label

AI-driven master data resettlement solution. Connects multiple information sources to identify, validate, format and enrich corporate data which is then used for omnichannel experiences

B&L has had strong growth in sales and number of customers in recent years, opening high-turnover operations in Peru, and starting relationships in Colombia and Mexico. We closed 2020 with a portfolio of more than 20 first-class clients, which additionally have operations in Perú and sales in México. We have over 12 large Retail companies, 3 Agriculture and mining holding, with over 45 subsidiaries with 5 user avg per subsidiary 225 users. Also we are opening our business in Europe this year.

Our target market are all companies which produce at least 6.000 critical data records (master data, customer databases, etc) a year and have an annual revenue of over USD $3M.

If we consider only the retail industry and focus on product data, on average a company handles 3.2 million rotating SKUs a year and has a yearly revenue of USD $1Bn in ecommerce. Taking into account our average ticket price of USD $4.2 per record and the total e-commerce revenue in Latam of USD $64Bn, then our minimum target market size is of 205 million records or USD $861M. If we extrapolate this to full retail (not only e-commerce), then the market size grows to USD $12Bn (taking into consideration Latam retail revenue of USD 900Bn).

In general, our solution is aimed to any company with an information system (ERP, CMS, MDM, etc.) trying to adopt a digital strategy in order to apply good data practices and reduce operational expenses or losses caused by carry-over errors in their data.

Gartner has estimated that poor data quality costs organizations an average of USD $13.3M a year, affecting labor productivity as much as 20%. 20-30% of operational expenses can be directly related to bad data. Harvard Business Review has estimated by adopting good data practices, companies could realize a 70% increase in revenue. With corporate data growing at a rate of 40% a year, trying to fix this issue using human resources alone has proven to be unsustainable.

We offer an AI-driven solution to standardize and enrich corporate data. Our proprietary algorithms allow us to extract information using technologies such as image recognition, web scraping and document understanding and use it as aggregated data to fix and enrich our clients corporate data. Our key strength is our vast historical data which grows as we work with more clients. We are not a data aggregator and as such we do not sell our database. Instead, we use our database to feed our ML models and help us in the data cleansing and enrichment process.

Data is the new oil and DataQ is the first automated data refiner. Currently tools such as ERP, MDM, CRM are vehicles to manage and govern data, but none is responsible for the information generated within them. , leaving this task to the different user profiles, with different visions of how this information should be created, which represents a risk for the company that can affect up to 12% of its net sales. With B&L all data that enters its systems or is published in any of its channels is correct, approved, complete and structured to be able to feed all systems and sales channels.
We are the sum of different technologies and the improvement of processes: The only ones in Latin America to use technologies such as document understanding and web scraping in the same collection process; allowing us a higher level of productivity than the competition.
We make scalable the creation of data and information that grows in the world at 40% annually, in the generation of new and updated data, ensuring its quality, structure and completeness. (A job that today is largely done manually, as a repetitive task that does not generate value given its high level of errors)
We deliver a customized service according to the needs of our clients, making the structures that feed all their systems and channels more flexible (we do not impose our way of working).
First to develop for the Spanish-speaking market.
Strength, conviction, drive, flexibility and ability to do this job anywhere in the world.

We identified several halfway solutions that don't sum up to the completion quality and usability that companies are in so much need, such as: image recognition like DataX, Appi vision, web scrapers as Import IO, MDM solutions as Stibo Systems, Akeneo, Informatica, Sales Layer, that only extract data, MKT Agencies that enrich with no escability or technology and Consultancies as well that can guarantee quality but not quantity, Internal Company Teams (Agustina del Valle) that generate high cost, low quality and poor quantity.

All of this 47% expensier than B&L solution understanding the number of services, quality, volume and pricing.
All other solutions can work as a complement for our solution from the sources point of view.

From our Benchmark Companies received a larger number of solutions a higher quality a exponential increase in volume of data for the best competitive pricing available gaining control of data quality, data completeness and data creation.

Our business model includes a SETUP cost that is paid upfront for the onboarding and activation of a new client, then they buy different options of data consumption per month paying a minimum number of data registry and there forward, in a 12-month subscription model with automatic renewal.
the SETUP starts from USD$639.66 up to USD$6,396.61 depending on the number of Data registry that they are looking to have in the first year and then the price goes for the number of data registry that they need to consume buy month, having one minimum that they pay if they use it or not and for everything above they pay the extra registry, with an unit value starting from USD $5.39 for registry down to USD $3.31 per Registry depending on volume. This leaves us with a 70% net margin.

For the next few years, we expect a sales increase starting at USD 1.000.000 for 2021, USD 2.138.941 for 2022, USD 4.577.333 for 2023, and USD 9.795.492 for 2024.
As we are developing our SaaS version, the annual cost will increase only by 30% per year independent of sales due to our crowdsourcing RRHH method and the technology involved.

3-stage sales model and support from the Marketing team: Pre-Sales, Sales and Customer Success.

Pre-Sales area generates over 96 new business contacts a week which provides the Sales area with an average of 3 new business proposals per week, keeping up a sales pipeline of USD $1.3M with a 27% historic conversion rate.

Our Sales team has a KPI of creating 1 qualified lead a week adding up to the pre sales team to increase traction.

Our Customer Success area makes sure customers are receiving what they hired, looking for new growth opportunities.

Marketing use strategies to increase leads an

Direct sales is the main customer channel acquisition, we have a commercial team with three differents focus:
Pre Sales: Prospection of new clients
Sales: Directly with potential clients
Post Sales: To engage clients reviewing constantly the success and seeing new need to generate up or cross sales

20% Equity for USD 1.000.000

One of the main risks our proposal faces is that MDM or PIM companies adopts its current solutions to offer a similar service, that is, decoupling certain data quality components used in your systems to deliver it as a package through a SaaS

ELEVATE, The Growth accelerator

Best solution or Service for eCommerce, eCommerce Day 2019, Santiago Chile.