Staying ahead of the curve in marketing becomes more challenging all the time. From perfectly tailored ads to personalized deals, customers have high expectations. Up-and-coming competitors are constantly nipping at your heels for market share. As margins are squeezed and brands fight it out for eyeballs, only the most innovative and agile can seize emerging opportunities and capitalize on unexpected trends.
That’s where data science in marketing comes in. By taking advantage of automation, predictive analytics, and alternative data, you can transform this avalanche of information into actionable, predictive insights. You can optimize your marketing spend and deliver better ROI. If, of course, you lock down the right tools, data, and strategy from the start.
Let’s take a look at how it’s done.
Marketers need data to drive business-critical use cases including better planning and delivery of digital and social media advertising campaigns, segmenting and micro-targeting audiences, personalization, and predicting emerging trends.
Depending on your specific objectives and the nature of your business, you will need different types of data to fill in the knowledge gap. For example, you may need data on customer demographics, online behaviors, sector sales patterns, geolocation data, or broader industry trends. Depending on what you are marketing, data on weather patterns might be particularly useful. Depending on where you are selling, you might need data on footfall or average incomes in a particular neighborhood.
The point is this: once you’ve figured out what you don’t know, or what your in-house data can’t tell you, you will need to figure out a data strategy. That means identifying what data sources you need to complement your own, where this data is held and by whom, and how you will obtain it and combine it with the data you have.
The important thing is to figure out which tools or platforms will simplify the process for you. Depending on the marketing and data science resources you have access to, your priority might be tools that facilitate quick and easy connections to pre-vetted, quality data sources. Perhaps you’re looking for something that will help you clean up and harmonize your datasets, so all the data you need is compatible. Or maybe your main concern is selecting and applying the most relevant algorithm for your machine learning model.
In any case, the key here is automation. From connecting to external datasets to feature selection and engineering, there’s simply no longer any need to perform fundamental data science tasks laboriously, by hand. In fact, with a comprehensive AI-driven platform, you will be able to automate all of the heavy lifting (and implement the right data science infrastructure for marketing), helping you get your models production-ready and delivering timely insights at speed.
That’s the basics covered. Now let’s run through the steps you need to take to build a data science infrastructure for your marketing needs.
What are the burning questions you need to answer about your marketing performance or customer base? Try to be as specific as possible. It helps (a lot) if you learn some of the lingo that will enable you to frame your marketing queries as data science questions. Telling a data scientist that you need a churn model, for example, will get you singing from the same hymn sheet a lot faster than asking them to help you figure out which customers are worth your marketing spend. You can find out more about translating marketing questions into data-speak here.
Is your marketing question really a data science problem? Will it require a machine learning model to make complex predictions about the future, taking multiple factors into account? Or could you figure out the answer by simply creating visualizations from some more straightforward historical datasets?
If it’s the latter, you might find that this is more of a data analytics or BI question and you don’t need sophisticated data science at all. Although, of course, even for BI it’s absolutely vital that you have plenty of high-quality historical data that’s accurate and relevant to your question. A data science platform that helps you access and combine external and alternative data sources may be extremely useful in providing that perspective, even if you don’t then use the data to create a machine learning model.
An effective data pipeline for marketing isn’t just about sourcing the right datasets on a one-off basis. You also need to think carefully and critically about how regularly you need to update your data and how you’ll feed this into your models. How quickly does your historical data lose its relevance and predictive power? If you’re responding to real-time changes in the market, you need to make sure there are no delays in getting the very freshest, most accurate data into your models.
Next, what data science resources and capacity can you leverage within your organization? If you don’t have data scientists at your disposal (or if they are caught up with other projects) you’ll need to factor this into your strategy. That means choosing tools that don’t demand data science expertise on your part to deliver great results. Consider investing in a robust data science platform or data-science-as-a-service (DSaaS) when it comes to building the right data science infrastructure for marketing, that works for you.
If you’re only just upgrading from being a data-driven marketing team to a data science-driven marketing team, these steps may seem a little daunting. The great news is that, with the right technology, they’ll feel a lot more straightforward. In fact, you can streamline and automate all four steps above with a top-end data science platform.
This will not only help you track down, connect to and combine excellent external data sources, but will also help you identify the most useful features to focus on, to deliver the most useful predictive insights. In fact, you’ll be able to automate every stage of the process, including selecting the right algorithm for your marketing question and keeping the model updated with fresh data.