5 Ways Data Science Can Optimise Your Marketing SpendVanda Williams
However, what has changed is that marketers can now look to analytics, figures, and campaign results to better understand their efforts and maximise their marketing spend.
According to a report by KoMarketing, 37% of marketers waste their marketing spend as a result of poor quality data. In addition, without an investment in data science, marketers can’t predict who their best customer is and which crucial touchpoints to hit based on a spreadsheet alone.
Data science can provide you with customer insight that you previously didn’t have; enabling you to pinpoint areas of improvement to maximise ROI.
5 ways to maximise ROI with data science
Below are 5 applications of data science for the ROI-conscious marketer:
1. Improve lead scoring
Naturally, your marketing budget will be wasted if you’re unable to identify and engage with the right people at the right time in their buyer’s journey. However, 65% of businesses admit that generating traffic and leads is their biggest marketing challenge.
As such, data science can help you revamp your lead scoring efforts by directing you to the leads that are most likely to respond to offer X, Y, or Z. Instead of guessing, marketing and sales teams will have access to data-backed insights to drive conversions. Data science enables you to get smarter with your marketing spend, minimise your response time, and increase productivity.
2. Estimate and optimise conversions
Although having a bigger reach is the goal of any brand, it doesn’t necessarily mean a higher conversion rate. Instead of looking at increased traffic as a sign of conversion, you should consider how relevant your messaging, brand, and products are to your audience.
Data science can help you understand what actions or exposure will lead to higher conversions. If you’re working with a smaller budget, this means that you can leverage your current assets and still maximise performance. Data science employs Machine Learning algorithms to precisely gauge what impacts conversions and what assets will perform the best for further optimisation.
3. Real-time personalisation
Without a doubt, marketing personalisation is a huge driver for success.
According to a survey conducted by McKinsey:
“Today’s personalization leaders have found proven ways to drive 5 to 15% increases in revenue and 10 to 30% increases in marketing-spend efficiency—predominantly by deploying product recommendations and triggered communications within singular channels.”
There’s also no deficit as to what you can personalise for your customers. Here are a few examples of facets worth optimising for higher cross-channel returns:
- Landing page microcopy
- Email offers and promotions
- Discounts and coupons
- Location-based campaigns
With data science, models can be developed for practically any kind of personalisation campaign. In effect, you can focus on improving your real-time offers based on customer data (or clusters of matching prospects).
4. Take advantage of prescriptive analytics
Through the analysis of raw data, businesses can make better, more informed decisions. Both small and large businesses can excel using data science by finding the best course of action in any given scenario.
As opposed to data monitoring, prescriptive analytics focuses more on actionable insights that your brand can take based on various data models. For example, you can determine how the weather forecast will impact buyer’s behaviours and PPC conversion rates if you run a campaign during a certain time period.
With insights into when the desired action will occur and what can impact it, you can control the various aspects of your campaign that directly drive sales and increase ROI. Additionally, the model can inform you of any variance and automatically pause the campaign when conversions drop below a certain limit.
5. Net marketing spend allocation
With the help of data science, you’ll be able to determine where to spend your marketing budgets and why. You can begin by exploring simple use cases and lines of questioning such as:
- How likely is it that leads in group A will convert?
- Which landing page results in the most conversions from group B?
- How does X impact the sales of product Y?
Not only can you utilise data science to unlock certain insights, but you can also then scale your research towards more complex problems. Moreover, you can reuse your consolidated and cleansed data for a plethora of data science models and apply different models in connection with it.
In effect, you’ll be able to build a viable, accurate and highly functional analytics network that can provide predictive and prescriptive recommendations on marketing spend allocation.