Back to basics: the 3 benefits of integrating Data Science into marketing
A reminder - what are we doing here? Video - the evolution of data science. Plus 3 articles I've read recently.
Hi dream team,
This week:
[Feature] A reminder - what are we doing here? The 3 benefits of integrating Data Science into marketing
[Uplift & Outliers] 3 articles that I read recently
[Video] MLs like teen spirit podcast - the evolution of data science
Back to basics: the 3 benefits of integrating Data Science into marketing
So what are we doing on the marketing department, other than eating cakes every time we hit a sales target etc? - Well we’re here to make smarter decisions, optimise marketing efforts, and build stronger relationships with customers.
It’s not easy. What helps? Data Science.
By integrating data science into your marketing, you can get valuable insights to create personalised campaigns, understand your audience better, and maximize your ROI by creating experiences and products that suit the needs of different customers.
But how exactly does data science contribute to your marketing strategy?
I felt like going back to basics a bit - so I’ll be diving into three key benefits of integrating data science into your marketing, and how it can drive real business results. Let's go!!
1. Campaign performance lift through personalised marketing
Personalisation is no longer optional. It's an expectation. Studies show that 80% of customers are more likely to make a purchase when brands offer personalised experiences.
This is where data science shines. You can go and find benchmark data from McKinsey that illustrates the upside of going big on using data for personalisation - see below.
But how does it work?
With data science, you can move beyond traditional demographic targeting (like age, location, and gender) and dive deeper into the behavioural patterns and preferences of your customers. By analysing historical data, customer actions, and interactions with your brand, machine learning models can segment your audience in a far more nuanced way, creating highly personalised experiences.
For example:
Predictive Analytics: Understanding when a customer is likely to purchase again or what they’ll engage with next based on their past behaviour (or similar consumers that look like them).
Dynamic Recommendations: Using algorithms to recommend products that match customers’ preferences, increasing the likelihood of a sale.
Let’s just zoom on on these two points a little more.
Predictive Analytics
In relation to what predictive analytics is helping us with, here is my take on the biggest issue - the problem → lack of data for most customers.
As marketers, we hear it all the time: hyper-relevance is the key to success. Tailoring every message to each individual customer, making them feel seen / understood, is seen as the ultimate way to drive engagement and conversions.
But here’s the truth that many marketers are facing: achieving hyper-relevance is super difficult when the majority of your customers are only making a single purchase. This is the Pareto Principle in action - go and check for yourselves - that 80% of your customers will likely only buy once.
If you're aiming for hyper-relevance, you’re relying heavily on data to inform your decisions. The more interactions you have with a customer, the more you learn about their preferences, behaviours, and motivations. This allows you to build your personalisation plan. But if 80% of your customers are only making a single purchase, that means there’s very little data to work with after the first interaction.
Predictive analytics helps with profile-assembly and the creation of a more enriched dataset to help overcome this - which you need to do to even begin to the think about doing better marketing. DM me if you need help.
Dynamic Recommendations
There are similarities and differences: I think I’ll do another newsletter about that shortly. But in the meantime - marrying up customer’s and their profiles with the best subset your product mix take a lot of data science.
Amazon's recommendation system is a big part of e-commerce success and one that we all recognise Amazon is driving significant revenue through personalised suggestions - approximately 35% of sales are attributed to its recommendation engine, which utilises machine learning algorithms to analyse user behaviour, preferences, and interactions.
A significant chunk of effort comes into modelling the diversity of the recommendation feed - the right mix of things that will:
Create conversions (short term revenue)
Engagement (keep you looking in the medium term)
New items that will stretch and expand your shopping categories (LTV, revenue growth over the longer term).
So a lot to “optimize” all at once, huh.
2. Improved customer insights
To market effectively, you need to understand your customers inside and out. Relying on assumptions or broad generalizations will only get you so far. Data science allows you to dig deep into customer behaviour and uncover powerful insights that can shape your strategy.
By leveraging data science techniques like customer segmentation, predictive modelling (see above), and sentiment analysis, you can identify trends that were previously hidden.
These insights help you:
Understand what drives customer decisions: From browsing patterns to purchasing behaviour, data science allows you to study dataset features to understand exactly what motivates customers to buy.
Understand segment behaviour and metrics: Using machine learning to look for unique segments can result in you exposing groups that are highly profitable, or groups that look like they have unmet needs (i.e. low retention). All this is not always seen with rules-based analysis or just “opinions” on who your customers are. Machine learning segments reveals multi-dimensional patterns in the data that you can’t see otherwise.
Tailor your messaging: Understanding customer preferences allows you to create more relevant, timely content, offers and products that is what your audience cares about.
The result? Better-targeted campaigns, product offerings, improved engagement, and stronger customer retention. You’ll be able to not only understand your customers but also anticipate their needs, making your marketing efforts more proactive than reactive.
3. Increased ROI through data-driven decision making
In the marketing world, ROI is pretty important and a regular topic of discussion. Every marketing pound or dollar you spend should be accounted for. I said should be - there are lots of challenges with attribution that are beyond the scope of this newsletter - and data science helps you.
Data-driven decision making allows you to optimise your marketing budget and achieve better results. Instead of relying on intuition or guesswork, you can use data science to back up your marketing decisions. Here's how:
A/B testing: Data science makes it easier to test and compare different marketing strategies to determine which one is most effective, ensuring your budget is spent wisely. Product analytics is an emerging area which blends experimentation, agile working, data science and a fast flywheel to try to generate performance lifts in shorter timeframes - A/B testing is often at the heart of this approach.
Campaign effectiveness: You can measure the impact of your campaigns and adjust them on the fly. If something’s not working, you’ll know and be able to tweak it for better results.
Attribution models: Data science helps you understand which channels are driving conversions, whether it's social media, email, search, or paid ads. This insight enables you to reallocate resources to the most effective channels, improving your overall ROI. Again this is a whole topic in its own right - there are a large number of dynamics behind understanding marketing capital investment.
With data-driven decision-making, you’re no longer relying on hunches or assumptions. Instead, you can make smarter choices that lead to measurable improvements in customer acquisition, conversion rates, and retention - all of which drive better ROI.
How to integrate data science into your marketing strategy
Now that we’ve covered the key benefits of integrating data science into your marketing strategy, you might be wondering how to get started.
Invest in the right tools: There are a variety of tools and platforms that can help you leverage data science in marketing, such as predictive analytics platforms, marketing automation tools, and customer segmentation tools.
Collaborate with data experts: If you don’t already have a data science team in-house, consider working with a partner who specialises in data analytics to guide you through the process.
Start small and scale up: Start by using data science for one or two key campaigns or processes, then scale up as you start to see the benefits. Focus on creating a data-driven culture within your team to ensure long-term success.
In Conclusion
The integration of data science into marketing strategies is frankly a necessity for most brands. The benefits are clear:
Enhanced targeting and campaign performance
Better customer insights
Improved ROI.
As the marketing world continues to evolve, those who embrace data science will be able to stay ahead, delivering more effective and personalised marketing activities, build using data and insight. Not to mention using data to deliver on the promise of creating value - now that our departments have invested in people and tech to make it a reality.
That said, I am old enough to have lived through the “Big Data” era (and perhaps we never really left it) - there needs to be a strong plan, solid use-cases, and all wrapped in execution skills and communication ability to really land data science benefits in marketing.
Uplifts & Outliers:
I keep up to date by reading a few things each week, here is a selection:
1) Zero-click search is collapsing the user journey for brands reliant on Google. How are B2B and DTC marketers responding?
👉 “B2B and DTC marketers find themselves on the zero-click search frontline” - this article explores challenges in digital marketing which data science can illuminate, particularly in search strategies.
Read more on Digiday
2) Big data provides CMOs a big opportunity to create value - if they can establish proven practices for organizing, analysing, and monetizing their customer data assets.
👉 “Unlocking The Potential Of Advanced Analytics To Ignite Growth” - this is a good read as as it explores the use of advanced analytics - a cornerstone of data science - in driving marketing success.
Read more on Forbes
3) You can improve sales performance by using data analytics to track, score, and convert leads more effectively.
👉 “How Data Analytics Improves Lead Management and Sales Results” - hands up anyone here from B2B marketing?! This article discusses using data analytics to enhance sales and lead management tactics.
Read more on Smart Data Collective
Video: MLs like teen spirit poddy
Since I’ve gone back to basics with the newsletter - I wanted to bring back the very first episode of MLs like teen spirit where Chris Pearce goes back to the start and talks us through the differences between AI and ML, then takes us on a tour through time, from when it was all just "statistics" into the complex, multi-disciplinary landscape we have today.
Thank you for the support folks!
Cheers,
John
PS - There are 2 other ways I might be able to help you:
MLs like teen spirit is our sister podcast show - I cover all manner of Data Science topics here, often with a marketing orientation.
I have an Online Store where I can be reached for calls or products (new, and a work in progress!)
Chief Data Hero is my other newsletter, I run this with some partners. This is for data pros who want to learn from those who have been there and done that in the data industry.