Creating Value From Data
Key Takeaways
Is Data Really That Powerful?
A lot of the startups that we have helped have second-time founders. These are people who have typically built up and sold their business and now, armed with that experience, understand the power of data and how it can be leveraged to gain a competitive edge. The fact that the second time around they opt to start building data infrastructure early, we feel says a lot about how valuable it can become.
While you might be familiar with the likes of Numan, Treatwell, MUBI, and Plend today, they were not big names when we first worked with them! By utilising data from the beginning, they were able to make informed decisions, optimise their operations, and scale effectively.
It’s important to note though that data is not a silver bullet to every problem. It needs to be closely aligned with your KPIs and key decisions in order to have an impact. No data product/solution is successful unless it is fully adopted and utilised by your business stakeholders.
Where Do You Start?
For most companies a ‘data-driven culture’ is a change from the norm, even now. And where there’s change, there needs to be management. This means that before you do anything, you need internal support from the c-suite, a project leader and a clear outcome.
Map out your customer journey to better understand your data touchpoints, KPIs and decisions across different departments. You will need a clear understanding of what is important to your business, and where the gaps are today before anything else.
With a sound understanding of your business needs, consider the technical side. You should think about expected data volumes, your existing tech ecosystem, any specific security or data privacy concerns as well as considering the cost when starting to plan out what you need to build.
When Should You Start?
For many small and medium sized businesses there are some major barriers though in implementing data. Namely, the cost of doing so, finding and hiring the right talent, and the complexity that is involved with setting up each part of the stack efficiently.
A mistake that we often see growing businesses make is that they try to hire one amazing person to handle their entire pipeline. The problem with this is that building a data stack requires a team with diverse skills and knowledge. This becomes costly later as often what has been built will not scale.
If you are early-stage then start with using excel or off-the-shelf data tools which help you get started with data quickly and without heavy technical knowledge. We typically recommend that once you get to the $2m, your businesses will require more customised reporting capabilities to gain deeper insights into your operations and optimise strategy.
173tech have helped many fast-growing businesses to leverage their data by setting up their data stack, extracting and modelling core data sources and creating dashboards. Often this acts as a best practice guide as you begin to hire internally.
What Should Your First Analytics Project Be?
Amidst the myriad of demands and priorities of different departments, pinpointing where to allocate resources can be challenging but one area which has consistently stood out in our client work when it comes to delivering value has been optimising digital advertising.
If every business in a market relies on the same algorithm for their ads, they will all be optimised to the same customer profiles and so instantly lose competitive advantage outside of increasing their budget and the quality of their landing pages. This emphasis on cost means that spending power becomes the most important differentiator in winning customers.
Quite often, the primary goal of and ad channels’ algorithm is to maximise conversions. Conversions are often set up by the user themselves but tend to include actions such as forms being filled, items being checked out or sales.This focus on conversion rates can drive short-term revenue and demonstrate immediate ROI, but it often fails to capture the broader picture of customer relationships over their entire lifecycle.
We have seen our clients significantly lower their CAC by sending their lTV back to advertising channels. Not only saving them money in lowering their CPC but also attracting customers who are more likely to spend higher amounts with them over a longer period. This data project often has a big $$$ value to it and can help to get people excited about the possibilities of data.
Using Data To Get Closer To Customers
When plotting out your customer journey, it is important that each lifestage is distinct from each other and that you have removed any overlaps or subjectivity. Once you start using data to automatically apply lifestages, it becomes much easier to understand your pipeline at scale and see which areas might need to be optimised, ensuring every step is fine-tuned for maximum efficiency.Customer tagging is a straightforward method to add your life stages to your database or CRM.
Customer tagging is also useful when considering segmentation. Effective segmentation should focus on distinct differences from a messaging perspective.Once you have established your segment, you can then track what proportion of your customer base they represent, their average lifetime value and their journey as a whole across your pipeline.
With our segments drawn in messaging lines, we can then cohort our users. This might be to bucket our customers in terms of value, activity or likelihood to churn. Cohort analysis helps us to understand the varying needs and behaviours within broader segments, facilitating more personalised marketing and retention strategies.
From here we can begin to apply predictive analytics, a way of looking at the past behaviour of your customers and then giving a percentage score as to how closely they match those people. As a customer takes these actions, flags can automatically be appended to their profile, giving your customer success or sales teams an early indication that this customer might leave.
How Best To Activate Data?
Typically a data model will be surfaced in reports and dashboards. A person then needs to understand and interpret that data, and make an action from it. While this sounds straight forward, it presents a number of challenges around data literacy, bias or just plain old human error.
When it comes to activating data, there is another route called Reverse ETL. In a normal ETL process data is extracted from its source, loaded into a data warehouse, transformed and combined through modelling into business metrics and then visualised. What reverse ETL does is it takes that modelled data and pipes it into any application. So you might use 4 different data sources to understand which customers are going to churn. Once you have that data modelled, instead of it living in a reporting tool, you can send it directly to a CRM for your team to intercept.
Once your data is centralised in a data warehouse or data lake, the next step is often to make this data actionable. Ensure your data is clean, well-structured, and in a format that can be easily extracted and loaded into target systems.
Using Reverse ETL ensures that every part of your company is reliant on the same data, acts as a clear guiding light on what actions need to be taken, removes manual errors and will help you democratise data easier as your teams don’t need to learn a new interface.
Conclusion
No matter where you are on your data journey, 173tech can help. We are a London-based analytics agency that helps growing businesses worldwide leverage data. Our core team previously led analytics at dating giant Bumble and we have since been using data to accelerate growth for the likes of MeUndies, Plend Loans, Wizards of the coast, MUBI and many more.
We work with your the tools and processes you’ve already established or can set up from scratch.
We deploy a full team to every project for speed and quality assurance consisting of: Project leads, engineering leads, data analysts and data engineers. You get direct access to our senior team through weekly calls and a shared slack channel. We work on everything from adding new data sources, migrating infrastructure or analysis projects.
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