Marketing Data for Tech Companies

Tracking your user from lead to advocate. 

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For an early-stage startup, it's fairly common to create a set of buyer personas. The process normally evolves from making assumptions about what types of companies are the best fit for your product, and then talking with the sales team to validate said assumptions. While this is a good start, it's very temporary and hard to scale. 

Having worked with both large companies like Shopify Plus, and smaller startups like Turnstyle and #paid, I've seen the various models you can use to collect and work with marketing data, and the success these models have depending on company size and structure. In this article, I'm going to dive into the idea of marketing data and the role it should play in your company. 

Data from Day One

We all want to make data-driven decisions. There's an allure to throwing stats at your colleagues when proposing a product, refuting their weak, emotionally charged objections with cold, hard, data. The issue, however, with many startups is that their data set of customers and leads is simply not large enough to draw a logical conclusion on what works and what doesn't. This raises the challenge of when to make the jump from testing an idea to proposing an approach based on data. 

For example, most startups that specialize in the B2B space are going to be very sales-heavy in their early days. If you don't have a large customer base, budget, or inbound traffic, it may not make sense to invest in areas of marketing like content creation, lead nurturing, and retargeting. While I do address that notion in this article, it is worth noting the difference between collecting data and actioning data. 

A Strong Foundation 

I like to think about the relationship like hosting an event. Tickets for a conference will go on sale months before the actual date, and a number of the finer details will change based on registration. A smart planner might change a specific session focus if a large proportion of the attendees are from one industry. Notice that the focus of the event is unlikely to change, but data is being collected from day one. 

 Changing conference seating based on attendees can increase engagement. 

Changing conference seating based on attendees can increase engagement. 

Likewise, if you are running a small startup and have yet to develop consistency in your client base, it doesn't make sense to make decisions primarily driven by data. In most cases, doing so will result in skewed tests. For example, you might decide that India is the ideal market for your product, because 5 of your 10 inbound leads are from India, as is your only paying customer. This is simply untrue due to the small amount of data you're working with. 

What you can do, however, is begin to collect data. In this post interviewing Steve Huffman (Reddit) and Emmett Shear (Twitch), this point is driven home. Huffman notes: 

There are a lot of corners you can cut when you’re building your MVP, but lack of data can haunt you. Think very carefully about what the minimum viable data is. You don’t even have to look at it, just log it somewhere, so you have it when you need it.

This observation has strong parallels to marketing. When you have the data (and budget) to make decisions about paid advertising and SEO, there will be a sense of regret if you don't have data behind how your early users came to your site, and what search terms have been hitting your site the most over your product's tenure. 

Beyond Lead Generation 

Having credible marketing data can stretch into making good decisions about your product in other areas as well. Using tools like Segment to connect marketing data with user data can be very telling about your user experience. Some basic applications could include: 

User onboarding: is there a noticeable pattern among your trial users that come from certain marketing channels? It's possible that a larger percentage of your users captured through organic search are dropping off due to misconceptions around what your product does. The solution might be to revisit the funnel of those users, and see how you can optimize your landing pages and messaging to get users that stick, and nurture those that won't. 

Identify pain points: as a marketer, it is incredibly intriguing and useful to see the pages that a visitor has seen before converting into a lead (give their email). What can be even more intriguing is the pages visited by your customers. Are there certain pages that are frequented significantly more by your highly engaged users compared to users that drop off, or vice-versa? Do certain assets (i.e. industry reports) increase user activity if downloaded by customers? 

 Mode Analytics addresses user drop-off with their tutorials.

Mode Analytics addresses user drop-off with their tutorials.

Basic Marketing Data 

Even if it's clear that all types of companies should be collecting marketing data, where do you start? I divide it into a few different types below, with mediums you can use. 

Visitor Data: Use a free tool like Google Analytics to connect your site and start measuring your daily views, popular pages, and more. If you're feeling adventurous, you can set up a Goal and start tracking inbound conversions. 

Lead Data: Sales teams need to be meticulous with their efforts early on, to see what works and what doesn't. Use a free tool like Hubspot CRM to manage your outreach. They will get an idea of the industries, titles, and other categories leads are in, and which ones are successful. 

App Data: Most developers already have some function of storing user data, though programs like Keen IO make it a lot easier. If your developers already store their data in a warehouse like Redshift, you can use Mode Analytics to interact with it.

Conclusion 

This is only a general outline, and it will take you some time to decide on what stack works best for you. More than anything, it is crucial to decide early on that you will be collecting marketing data, and have it organize in some way. As long as you have a key (i.e. email) or other token that you can use to link your data together, it will be significantly easier to make data-driven decisions when the time comes. 

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