5 Key Metrics For DTC Founders
What metrics really matter? Since the inception of 173tech, we have spoken with hundreds of businesses about their data challenges and this is one of the most common questions we get. We’ve helped many of the fastest growing direct-to-consumer brands discover growth opportunities from their data, so in this series of articles, we put ourselves in the shoes of a DTC (Direct To Consumer)founder and walk through the top 5 data insights that every founding team needs to know and tips on where to find them.
Why It’s Important.
When it comes to seeing ROI from your data we always say to “follow the money.” By having a firm grasp on your sales figures, you’ll quickly understand if revenue is increasing? From where? Which segments are growing / not doing so well? These are the key questions for any business and its absolutely essential that you You have the most up-to-date and 100% trusted data at your fingertips.
Clear Revenue Definition.
This might sound like a trivial point, but making sure your revenue is defined consistently across all teams can save you a lot of time and headache doubting and reconciling your numbers. Below are a few considerations when defining revenue metrics:
Base: Gross Sales = Sales Price * Items Sold
Additional Components: Taxes, shipping, returns, discounts. Which one(s) should be added to your revenue calculation?
Accounting: When is revenue recognised? Point of an order generated or fulfilled or delivered? What about returns?
How Do I Analyse My Sales?
Total sales over time is good to know on a high level but more importantly you want to know where you are growing vs. stable vs. declining. This involves breaking down your sales data by different dimensions and comparing the trends from different segments. The most common cohorts are: country, product, and customer acquisition channel.
If your data is modelled well with a self-service reporting tool on top, you should be able to easily analyse your sales by any factors collected in your data, e.g. SKU, product colour, marketing creatives etc. You can also build algorithms that automatically detect segments that are trending.
Are You Meeting Your Sales Target?
It helps to aim towards a concrete goal, e.g. £10m run rate. Your sales targets should be ambitious so it motivates but not out-of-reach as constantly missing them could cause low morale in the team.
Your sales targets should be centralised and visualised together with your actual sales so everyone has up-to-date visibility. One way is via automated CSV or Google Sheet import. You may also want to break down your sales target in strategic areas and allocate responsibilities to teams.
Who Are My Customers
A deeper understanding of your customers beyond demographic breakdowns (e.g. age, gender, country etc) can help you tailor your marketing efforts, build stronger brand affinity and loyalty, and increase customer LTV.
What Do They Buy?
Can you find patterns in their purchases? Is there a particular product that is very effective at bringing in new customers? Once they have enjoyed their first purchase and get to know more about your brand, what else do they buy? Are there products that tend to be purchased together?
A very successful DTC strategy is to find your star acquisition product, this could be a niche or small-ticket item for high AOV brands (e.g. an eyelash curler) that is very cost-effective at attracting new customers. Once they convert, continue to nurture them with tailored content and try to upsell to more and higher value items (e.g. face serum).
What Do They Feel Strongly About?
A study from Accenture found that 52% of consumers are more likely to buy from a brand with values that align with their own. That is great but how do you know this for each customer and at scale? As a successful brand, you are likely to have multiple unique selling points (USPs). Which one(s) attracted the customer to your brand and how can you leverage this information to build strong relationships?
There is no way to deduce it accurately for everyone but there are signals already in your data and you can deliberately gather more. If your marketing data is well-structured and integrated, you will be capturing which USP a campaign or ad or creative is advertising. If a customer converts from a campaign about your products being zero-waste, you could flag him or her as eco-conscious. If someone converted from a discount code, they are likely to be a value-seeker. These are rich data points that should be captured in your data pipeline and made available to all teams to analyse and create dynamic customer lists for tailored CRM.
With all the above data enrichment, your marketing team can start creating customer personas and tailored marketing strategies. Make sure to automate the persona definitions in your database, so you can easily analyse sales / LTV per persona, create dynamic lists and evaluate campaign performance by each persona.
As the old adage goes ‘you need to spend money to make money’ but how do you ensure that you are spending money in the right way? How do you measure the cost of acquisition?
The classic way to do this is a calculation of CAC vs LTV. CAC or Customer Acquisition Cost is the cost of winning a customer to purchase a product or service, and LTV stands for Life Time Value and is an estimate of the average revenue that a customer will generate throughout their lifespan as a customer.
Your shareholders and investors are likely to only want to know the blended figures on a very top level. To optimise your spend, you need insights with more granularity. Here’s a quick calculation which will help you understand which channel(s) bring in the highest LTV customers at the lowest cost..
Hopefully you have cross-channel spend and first-party attribution data integrated already. The next step from here is to get an estimate of customer LTV as an early signal on marketing ROI. (i.e. LTV / CAC)
You can create a simple yet effective rule-based prediction model based on historical data or estimations for a new product.Where you have little or no data, it’s often better to use rough numbers so long as they can guide you. (and you’re aware they are only a guide)
You can go further by calculating the time to cashback. For example, you know the following for customer acquired via Facebook:
Customer Acquisition Cost = $50 Estimated Lifetime Value = $75 Average Order Value = $25 Average Monthly Orders = 2 Then: Estimated long-term marketing ROI = Estimated LTV / CAC = $75 / $50 = 150% Time to cashback = CAC / AOV / order frequency = $50 / $25 / 2 = 1 month
You could also do the same calculation only with Estimated Lifetime Profit and Average Order Profit instead (aka your unit economics ) which enables you to optimise on what really matters, money in your pocket.
Most businesses recognise the importance of keeping their clients, after all:
- Acquiring a new customer is 5 to 25 times more expensive than retaining one.
- The probability of selling to an existing customer is 60-70% but only 5-20% for a prospect.
- Improving retention has a 2-4 times greater impact on growth than acquisition.
So what metrics are important for you to understand your retention rates?
Are They Coming Back? And For How Long?
Before you dive into retention optimisation, it’s important to understand the current behaviour and drop-off points. Perform cohort analysis to see how frequently are customers coming back and when certain segments churn. For example, you might find the majority of customers who purchased an eyelash curler as their first product do not come back and make a second purchase. With this information, you can then brainstorm ideas to bring them back.
CRM Strategy & Smart Personalisation
You should have CRM “flows” designed for different stages of the customer journey: a welcome flow, order updates, out-of-stock, product updates etc. They can be broken down into sub-flows tailored to each customer segment or personas.
What is a flow? It’s a series of business rules that give clarity to your team on what next steps to take in different situations. While the flow of “what” to do may be broadly similar for all your customers, by personalising how it is delivered, you can greatly affect the results.
Let’s say you have a group of customers who are flagged as being eco-conscious customers, and you have a score that tells you which have a high-probability of churning. While you might have a flow that tells you how to try and win back at-risk customers, by tailoring your message to include promote the eco-friendly nature of your products, you’ll have a higher success rate. Although, make sure you don’t over personalise as it can make customers feel being stalked and create privacy concerns.
Another rewarding strategy we have seen from DTC brands is building online communities that unite customers around your core values. This could be Facebook groups, Discord chat rooms, or forums hosted on your own platform. Create Natural Language Processing (NLP) models to automatically turn unstructured text data into trending topics, sentiment score towards your brand and measure overtime. The rich qualitative data will not only help you with new ideas on how to retain customers but also in acquisition and conversion.
This may be difficult depending on your area, after all few people may want to have ongoing active discussions around most FMCG goods and so you may need to widen your subject matter somewhat.
How 173tech Can Help
While all of the above seems simple on paper, ensuring you are getting the right right metrics across channels, files and programmes is often difficult. Information often sits in silos and it’s only through combining and modelling it, can you extract the hidden value.
We build & optimise your data stack, turning that data into decisions that will fuel growth. Everything we do is bespoke to you, owned by you, and fully scalable. Get in touch today to find out more.