Data-Informed Product Marketing
In practical terms, this is the optimization of product marketing efforts based on customer data. With deep knowledge of the customer, you can personalize messaging, campaigns and tactics. Data also help with anticipating the future need of the customer based on present behaviour.
For example; a customer who regularly buys valentine cakes is most likely in a relationship. If three months later, they order a cake for an engagement party, you know a wedding cake will be needed soon. In 12 to 24 months, that customer may need a birthday cake for their kids or partner. Understanding their present behaviours provides you with the information you need to anticipate their needs and personalise their offerings.
Common Challenges with Data-Informed Product Marketing
1. Data Privacy issues
Customers are less willing to share personal data. This may make it difficult to get the information you need. One way to solve this is to build a KYC feature into your product. Provide a means for customers to identify themselves at the point of registration or usage. Clearly state what the data will be used for.
Another way to get the information you need is to directly ask for it. Reach out to your customers for short interviews or reviews, this can be done via phone calls or in-app requests. Ask questions on social media, and search for what people are saying about you. Get the data people are willing to share.
2. Gathering relevant data.
The presence of multiple sources of data may be overwhelming and confusing. From Social media to your CRM, and sales team. One way to circumvent this is to have an objective in mind.
What do you want to know and achieve with this data?
A clear objective will help you filter the information at your disposal.
3. Generating Insights from Data
Beyond dashboards and charts, your data should inform strategic business decisions. The insights you generate from your data are more important than the skill to collate and organize data. How do you solve for insights?
- Be revenue minded.
- Deep dive your data; use the five whys method
- Have a clear objective.
- Liaise with an in-house data team if possible.
Important Data to Track in Product Marketing
The data relevant to your product is dependent on the product, business goals, and campaigns. However, these are some of the important data to track as a PMM.
1. Customer demography
It’s important to know the audience you attract. In instances where your acquired audience is different from your ideal audience, you may need to question your messaging, positioning and targeting.
2. Time to value (activation).
The time it takes your customers to perform the first profitable action is an indication of retention lifetime value. Customers who discover the product value early are more likely to re-use and refer.
3. Daily active users (DAU)
The daily active users give you insight into the usage of your product. Customers who do not use your product are more likely to churn. Tracking DAU can help you engage customers just before they churn.
4. Monthly active users (MAU)
This is similar to DAU but from a monthly perspective. As a PMM it is not enough for customers to signup or downloads your product, their monthly usage is a critical indication of retention, lifetime value, and revenue.
5. Net promoter scores (NPS)
The Net promoter score shows you how likely it is for a customer to refer others to your product. It is an indication of customer satisfaction. Satisfied customers become brand/product advocates. It is important to measure customer satisfaction and to collect feedback for product/service improvement.
6. Marketing Qualified Leads (MQLs) and Sales Qualified Leads (SQLs)
MQLs are prospects who have been verified by the marketing team to be suitable for sales. These are leads gained from marketing activities who are likely to purchase the product. SQLs on the other hand, are prospects who the sales team can confidently convert. They’ve moved down the marketing funnels and are ready to be customers.
An example of an MQL is a customer who requests the demo of a product while an SQL is a customer who is satisfied with the demo and wants the product.
As a PMM, it is important that you measure the MQLs and SQLs as this can be an indication of the quality of your messaging, targetting, and campaigns.
7. Revenue
The essence of marketing is to grow a business; attract customers and generate revenue. Therefore, the ultimate measure of success is the revenue generated by your product. It is important that you tie all campaigns to revenue generation and understand how every action leads to revenue for the business.
Sources of Data for Product Marketers
The first source of data is your CRM. This is where all the information about your customer lies. Depending on your product and CRM tool, you should see information about customer demography, purchases/in-app interactions, frequency and recency of product usage.
Analytics tools such as Mixpanel, Firebase, Google Analytics, and AppsFlyer amongst others can show details around in-app activities tied to customer details, campaigns, and seasons.
It is important to tie app activities and usage to campaigns and customer profiles. With this, you know the best performing campaigns beyond clicks and impressions.
Benefits of Data-Informed Product Marketing
- It helps with precision marketing; targeting customers who are more likely to use your product based on the knowledge of existing customers
- Personalisation. Customers are more likely to engage with messaging that speaks to their needs. This study shows an increase in conversion rates due to personalised content.
- Optimize best performing channels. Campaign data helps you know the best channel for engagement and conversion.
- Reduce acquisition cost and optimize ROI. This is the outcome of all the previous benefits listed.
Conclusion
Growing a product is easier when you have in-depth knowledge of the target audience, the market, the solution, and the customer preferences. Data-informed marketing is precision marketing. It saves you time, money, and effort.
