Predictive analytics is a valuable tool for business growth opportunities, offering a business the ability to improve present-day decision-making by offering a glimpse into future customer purchasing habits. It’s like using foresight and not hindsight to make decisions.
Enabling predictive marketing can be a challenge due to the huge amounts of data generated from various devices and digital channels. However, done right, predictive analytics can provide significant benefits to marketing teams and service teams by correlating data to improve marketing strategies, campaign goals, service and retention, KPI measurements and ROI.
what is predictive analytics?
Predicting the future with 100% accuracy remains out of reach no matter how much data you’ve got to play with. What can be achieved, however, is to forecast with a good degree of accuracy future customer buying patterns and action-based purchasing decisions using predictive analytics methods and modelling.
What is Predictive Data modelling?
With predictive data modelling, business customer data is gathered from in-house and third-party data sources and is analysed to uncover customer browsing patterns, purchasing journeys, and other key indicators. This information can be used to identify likely outcomes in various customer behavioural situations.
With these data insights, marketing teams can optimise their marketing campaigns to meet the target audience’s customer and buying journey.
Imagine if Mcdonald’s new exactly when each potential customer was going to want a Big Mc and they could send a personalised message to that person in that time frame. Imagine if that message also contained a 1 time-based discount for a Big Mc.
Is this Artificial Intelligence?
Yes, combining the power of artificial intelligence (AI) and machine learning (ML) with existing customer data creates the valuable, data-driven insightful marketing teams need to attract, retain, and nurture customers.
how can predictive analytics help in creating succesful marketing and Service campaigns?
No doubt, big data is the fuel that powers modern marketing decisions. Predictive analytics plays an important role in converting this data into actionable insights that improve the quality of audience segmentation, targeting, and promotion efforts. Here are some examples of how marketing teams use predictive analytics to improve their marketing effectiveness.
Accurately predict consumer trends.
Consumer trends and their choice preferences are constantly changing due to global media, large shifts in social bias and global influencing trends pushed by influencers and news outlets.
Predictive analytics has the ability to analyse data from many sources, including contextual data such as weather, location, world events, consumer sentiment, and digital social content trends being shared, discussed and streamed. .
Predictive analysis can spot emerging trends quickly, giving marketers a distinct advantage.
With this information, marketers can create customer segmentation in different ways to improve targeting and, ultimately, deliver personalised and now hyper-personalised targeting and retargeting campaigns to existing customers and prospects.
Create highly customised campaigns.
Delivering the right message to the right customer at the right time requires automated customisation.
Predictive analytics helps marketing teams better understand the behaviour of individuals and more accurately predict which messages are most likely to resonate with which customers, at what time, on what day and on which platforms.
Reduce customer churn.
It’s a fact that attracting new customers is way more expensive than retaining existing ones. Predictive analytics can spot trends in existing customer disengagement, providing marketing teams with an opportunity to improve customer service areas that may be performing poorly like 1st and 2nd line service response times, complaint resolution times, social media brand sentiment and 3rd party review data.
Customers that are likely to churn in the future can be identified. Once identified, these high-risk customers can be placed into a re-engagement. /customer retention program that provides personalised, time-based promotions designed to reduce attrition and increase long-term loyalty and increased revenue forecasts.
It’s worth saying that dealing with customer churn in this way is only a tool. Creating and maintaining a culture of providing amazing customer service across the board would be the core strategy for any business.
Predictive analytics and other related technologies, such as artificial intelligence (AI), natural language (NL)and machine learning (ML), will continue to play an increasingly important role in personalising and very soon, hyper-personalised marketing activities.
Hyper-personalisation is the use of real-time data (and lots of it). AI, ML NL and predictive analytics are used to get better information from your audience at an individual level and, in turn, provide businesses with the option to act on this structured information.
Starbucks uses hyper-personalisation tools to find a customer in its database and send them contextualized (location, behaviour, preferences) messaging at the optimal time and place as an act of product targeting:
“Hi Lee, as it’s Sunday morning and a little cold out there, we thought you might like a hot chocolate to warm you up – Don’t worry, this one’s on us!”
Using predictive analytics to solve marketing problems.
Predictive analytics is being used to resolve some of the marketing industry’s most pressing challenges. Here are just a few.
Improving marketing resource allocation.
Predictive analytics tools enable marketing teams to use their existing resources way more efficiently while also increasing ROAS and overall ROI.
By accurately predicting customer behaviour and adopting personalised segmentation, marketers can create highly effective marketing strategies and highly targeted campaigns specifically for those most likely to take the desired marketing action. This is an extremely cost-effective customer acquisition strategy.
Recommendation engines are designed to maximise the value of each customer and are used at various stages in the buying journey to suggest products highly likely to appeal to each individual consumer and can increase the average value of an order significantly.
Recommendation engines make recommendations based on a shopper’s purchase history data, lifestyle data, and other information to recommend only those products or services that closely align with their interests. Predictive analytics is used to combine relevant data and identify products that meet this data match.
Providing a seamless, engaging omnichannel (360) experience is essential to retaining existing customers. Today consumers interact with brands across multiple touchpoints including mobile apps, e-commerce websites, social media, and in-store visits and the critical 3rd party review.
Predictive analytics connects the data generated from each customer interaction, providing marketing teams and customer journey teams with qualified data-driven insights that can help to improve overall customer experience.
Preditive Marketing - Final Thoughts
As technology continues to advance and data continues to drive virtually all marketing fundamentals, the role of predictive analytics will only become more pivotal.
It’s not just a tool for the future; it’s already a fundamental driver of marketing and service innovation that is already woven into many consumer purchasing decisions and customer journey touchpoints.
Predictive analytics is a powerful set of tools that enable accurate, data-driven decision-making for businesses.
Instead of hindsight, marketing teams and service teams can now use foresight to make informed choices, produce optimised marketing strategies, and more efficient customer acquisition campaigns and provide a more proactive and personalised customer journey.
Hyper-personalisation is the next wave, it’s actually already here!