Is Your Startup Making Data Driven Decisions

by | Jun 9, 2020

As our startup has been growing rapidly over the last few years we have consistently been faced with having to make difficult decisions on various aspects of the business such as markets for expansion, marketing spend, staff performance, key customer segments etc. These decisions are usually driven by a combination of strategy, budget, networks and many other factors, but we felt that there had to be a better framework to make these decisions.

One of the key challenges we wanted to solve was how to align all of our teams’ activities to our organizations goals and thus measure each individual and teams performance. So we started with this problem and quickly realized that this wasn’t just about filling numbers in an excel sheet but required a better way to track metrics.

Why Data? Being able to improve on anything starts with being able to measure that improvement (read more on SMART goals). This comes back to measurable metrics or ‘data’ to track progress against a bench mark or goal. Data can be very very valuable asset to any organization (#DataIsTheNewOil), but its hidden and most organizations aren’t maximizing on this.

Challenge 1: Do we have any data? As a technology company we are lucky that our systems are digital and there is usually some trace or record of any transaction that happens on some system. Many organizations don’t have this luxury and a lot of data is still non-digital (remember paper) or not stored at all. We’ve visited or talked to many customers across Africa to show case our communication platform and are always surprised when potential customers say that they don’t even have their clients’ contact details organized.

Challenge 2: How do we organize this data?

The second major hurdle organizations face in using their numbers is that how to put it together. A lot of organizations, ours included, face the challenge of having multiple systems in place. This could be legacy systems, systems for different functions e.g HR, Sales, Support or just data scattered within one system.

Challenge 3: Is your objective clear?

If you already have some data, the second step is to understand what problem or improvement we are trying to make. In our case it was trying to measure team performance and align with organizational objectives. This is still broad. The problem statement needs to be as granular as possible and unique to your organization. For instance trying to reduce the number of failed online transactions by 5%, trying to increase customer retention by 15%, reducing average support response times by 20mins. Based on this problem statement or goal, one can start to analyze the existing data sources and how to get value from this data.

Can I Make a Decision Now?

Not yet. There’s still many details to figure out.

  • Can the bench mark be measured easily with a single data point or it has to be aggregated from multiple points?
  • Do all of these points reside in the same place (database, excel file, system, department, cloud/on-premise etc)
  • Can these be pulled regularly and easily manually or requires some automation or tool?
  • Do other stakeholders within your organization need to be involved?
  • Is the technical effort worth it? Is there a easier workaround?
  • How can these be visualized easily for quick decision making and analysis by non-technical users?

A lot of steps, yes, before we can get value from this data and track our progress.

In our case we decided to tackle a smaller segment first by gathering all data points just for our sales teams to improve their KPIs. We implemented this through a better CRM which captured more data points, and are working on combining this with sales conversion records triggered from our internal systems and user signs up from our website.

What we’ve also realized from this process is that we need to start tracking a lot more activities to gather as much data as possible. Whether its sales touch points, granular user clicks on our web or mobile applications, support queries via offline channels. We will also start exploring how machine learning and artificial intelligence can be put to use to get more insights from these large data sets. All of these will not only help solve internal challenges to help grow our business, increase productivity and improve our customer experience but also give valuable tools for our customers to make better communication and marketing decisions.

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