There is no disputing that technology is driving rapid change in almost every major industry across the globe especially autonomous email, predictive personalisation and data analytics. Cloud computing, artificial intelligence, machine learning, blockchain, and the IoT are just a few examples of technological advancements heavily disrupting consumer and B2B markets at an unprecedented pace.
In the marketing industry, companies now have access to more consumer information than ever before, which would lead you to think it would be a cakewalk for them to pinpoint exactly what each individual consumer is and what they are will buy next, and the ideal channels to engage them through. Yet, it’s not that simple.
That’s because one of the great challenges companies face when adopting new technology is figuring out what to do with the copious amounts of data they have in their possession. In fact, studies have shown that data complexity ranks as one of the biggest marketing challenges today as companies struggle to find any real value from this exponential increase in data availability.
Fortunately, the emergence of big data analytics offers an excellent solution to this problem since it provides a simple framework for examining and digesting large data sets in order to derive valuable, actionable insights.
What is big data analytics?
Big data analytics is the process of using advanced analytical techniques to study large data resources to uncover hidden trends, patterns, and correlations that could potentially lead to actionable insights. When utilised correctly, big data technology can provide clues that lead to more intelligent business operations, increase efficiency, improve customer satisfaction, and helps to create and maintain a competitive advantage in the market.
As worldwide email use becomes more popular each year, marketers are now turning to big data to help them inform their email marketing strategies to improve ROI and drive more revenue through their businesses. Here’s how.
Predictive personalisation empowering ecommerce success
If you want to maximize ROI from an email marketing campaign, then each and every email must be presented and structured correctly I such a way that it is unique and personal to the recipient. By this we mean the selection of products offered in communications to them being exactly where that consumer is in their selection criteria, in terms of their lifetime interaction with you. You don’t need to receive details of a red jumper if you bought one on Tuesday, what’s worse is what it tells you about your retailer that they didn’t realise this. They are far more likely to want the scarf, coat and gloves that match or complement it from their preferred label.
That means optimising product selection criteria, nailing the highest buying propensity ranking, and ensuring you have the timing of such communications as proficiently tied down as possible. Predictive personalisation will help take your email marketing to the highest returns in ecommerce marketing. It was already delivering 6x (or 7x according to research from Experian), personalised product selection emails are however now reported to generate a 20x (20-fold) more revenue than generic emails, omni-channel marketing and social-media platform marketing combined, by such illustrious research companies as McKinsey and Forrester, such is the power they now command.
Big data analytics facilitates this personalisation by giving companies access to important information about the people on their database, including not only buying history, but their buying process, and tail-tale signs that identify the degree of interest in particular products, and remembers how they arrived at a successful purchase previously.
By directly appealing to each customer’s wants and needs (as well as addressing them personally), marketers will have a significant head start over the competition. Their interaction with your site is unknown to your competitors, regardless of google ads. Therefore taking advantage of this data, promptly offers a huge advantage to the retailer, that was previously going to waste.
Predictive Data Analytics
From time to time, the role of a marketer requires them to look into the future and anticipate upcoming trends rather than focusing on what’s current. This is because forecasting future demand can provide valuable business insights that can help direct forthcoming marketing campaigns to improve their success rate. Big data and artificial intelligence can also be used to better predict consumer buying habits. For example, businesses can use these tools to predict the likelihood of a future purchase of specific brands and styles or whether or not your should adopt new product lines or brands.
Autonomous Emails
With the power of big data, companies can develop more accurate automation strategies that the advent of predictive personalisation enables the process to be wholly autonomous, rather than necessitating waiting for triggered consumer responses. Triggered solutions may include sending a personalised welcome email when someone subscribes to your website, or sending a cart abandonment email when a customer is contemplating a purchase, but nevertheless while profitable they are massively lower down the scale of ROI in comparison to PPS software.
Revenue Attribution
Email marketing attribution refers to the practice of deciphering whether or not an email was, in fact, responsible for a conversion. However, this is a notoriously complicated task since consumers are typically exposed to a plethora of marketing and sales correspondence before they make the final purchase. With that said, revenue attribution is a fundamental aspect of marketing since it enables companies to fine-tune their strategies, dropping the parts that don’t work while doubling down on the areas that yield the best results.
Thanks to big data, companies are now able to instantly measure the impact and overall effectiveness of their email marketing campaigns, and it’s all done in real-time. With sales data feeding directly into the CRM, big data tools can more accurately attribute specific marketing elements that contributed to the final sale.
Predictive personalisation solutions, as well as operating autonomously gather instant data on the effectiveness of their ability to perform, as that response in intrinsic to their next immediate reaction. It self-perpetuates in an ever increasing volume of data on each consumer. If it sends their highest likely propensity by algorithm calculation, and the consumer buys the second most likely, then it remembers this and machine learning incorporates this into the future cipher.
Conclusion
Big data has changed the marketing game for good. Now that companies have finally found a way to harness the immense volumes of data they have at their disposal, they can create much more effective marketing campaigns thanks to increased personalisation, automation, and predictive analysis. All of these factors assist in the formulation of high converting marketing ROI and drive yet more revenue to the business while improving the customer experience.
