5 ways to apply data science to your marketing efforts and get better results
It’s rare to come across a marketing department that isn’t leaning on Big Data to drive its decision-making in some shape or form.
Data science is intertwined with organisations’ consumer-facing efforts. It’s having a major influence on areas like customer service and ecommerce sales, as well as how marketers communicate with potential buyers.
But not every department knows how – or has the skill set – to dive into a campaign and uncover the truth about how it performed and its impact on the business. Pairing a customisable platform with these five approaches can get you started on the right track.
1. Data visualisation for informed decision-making
At the end of the day, Big Data is only as useful as marketing teams make it. The numbers don’t inherently hold any particular value; instead insights come from the skilled data scientists manipulating them.
Data visualisation is key to making the information palatable. Dashboards can be customised to provide a brief overview of a metric, a comparison of two competing channels or an in-depth analysis of one. It’s a simple, yet fundamental, step of effectively using data science to improve a marketing department’s performance.
2. Leveraging sentiment analysis in customer service
Do you remember how you first felt after trying your favourite chocolate bar or after watching a TV show that you’ve since sworn off? Those feelings of elation or apathy can create lifelong customers or sworn enemies, and marketers are using data science to figure out what they can do to elicit the emotions that are most financially beneficial.
It’s a lucrative possibility for businesses which have caught on, and a potentially costly one for those which haven’t. Following a positive experience, 85 percent of consumers involved in a McKinsey & Company survey revealed they purchased more than they normally would. A negative interaction led to 70 percent spending less on the brand in question.
Net Promoter Score (NPS) is a simple metric that can be paired with more advanced customer sentiment analysis to help steer marketers in the right direction. For example:
- Product rollout: Identify the words that positively represent specific inventory to craft a message that will be well-received.
- Email campaign generation: Develop different messaging for each audience – negative, neutral and positive – to customise content and reflect their stages in the buyer life-cycle.
- Public perception: Assess whether recent changes in service are having an adverse impact on customers, and design a strategy to specifically combat that.
3. Using social media listening to improve customer personas
The industry has long since moved on from general marketing campaigns and towards an approach that leans on comprehensive buyer personas, and catering to those niche audiences. Building an outline of these theoretical consumers is the easy part – finding an edge and yielding improved conversion rates is much more difficult.
Active social media listening is being used to supplement conventional market research. Dashboards can be used to monitor conversations on major networks like LinkedIn, Facebook and Twitter in a bid to closely capture opinions and create a repository of relevant content that writers can draw from to enhance the personal connection that shoppers have with the copy and brand.
4. Predictive analytics for ecommerce
A crystal ball would be a great tool for marketers, but predictive analytics will have to suffice in the meantime. Data modelling can help companies with a wide variety of tasks, such as spotting cycles in consumer trends that identify the most opportune times to run specific marketing strategies, or determining the appropriate channel for a campaign.
Organisations can pool both internal and external data and leverage machine learning algorithms to accurately predict the best course of action to take. In ecommerce, this could be applied to inventory by running marketing campaigns with data-backed surges in sales.
Although there can be a steep learning curve attached to leveraging predictive analytics, the result of a successful strategy is enticing. Research suggests that those who prioritise it are nearly twice as likely to convert high-value customers over those that don’t.
5. A/B testing web copy
Marketing teams can use data science to consistently evaluate their landing pages, email campaigns or sponsored copy by creating two versions with distinct deviations in style and wording to understand what words trigger higher conversion rates.
The practice has grown popular over recent years, but with the help of machine learning algorithms, companies can get more granular insights down to the difference in impression that a single word makes.
LinkedIn used this strategy and found that something as simple as using the word ‘register’ instead of ‘join’ led to a 165 percent higher click-through rate.
Small changes can make all the difference – the power is having the data to make it work.
Gain an advantage with data science today
If you haven’t already considered how data science could be applied to your marketing team’s efforts, you’re behind the curve.
Don’t worry – Statwolf’s team of experts can help you find the right solution that’s customised to your every need.