Drugi jezik na kojem je dostupan ovaj članak: Bosnian
The most crucial trait of successful marketing strategies of today and tomorrow is that they have to become customer-centric. Many marketers believe that they know their customers, but in essence they are just guessing, depending on traditional methods of analytics, while the end user eludes them. Gone are the days when communication was conducted with wide demographics, as the data possibilities readily available today offer the means to understand your customers, anticipate their needs, and engage them individually, and personally. Marketing is no longer a guessing game, but a data driven journey, and a big part in generating the insights necessary for making your marketing a customer-centric journey is played by predictive analytics.
According to recent studies, the vast majority of CEOs believe they are delivering a superior customer experience (80%) but only 8% of their customers agree with that! Today’s customer is highly distrustful of company ads, and instead depend on peer recommendations to drive their purchasing decisions. In a market with such consumers, you have only 2 seconds to connect to a customer, and all it takes to succeed in that is to have at least one personal touch that will have a major impact on the customer’s perception of your brand. This is where predictive analytics come into play. By gathering relevant customer information and using predictive analytics to determine the right offer for the right customer at the right time, you can provide the personal touch that will resonate with the customer and draw them to you.
How does it work? Well what better way to describe it than with an infographic:
In this way, what you actually do is use real-time data on the habits and needs of your customer to enhance their consumer journey and add to the post-purchase experience, creating the conditions for gaining a loyal customer. However, with traditional analytics tools that analyze the past for a better understanding of exactly that, the past, you can’t provide the actions described above. Statisticians used to have to rely on a statistically representative sample of the population in order to test their hypotheses and then draw conclusions about the entire population, but such an approach only gave an estimate of what was relevant before that point. What about the future? Well, thanks to significant advancements in data storage and processing capabilities, customer insight (CI) professionals now have the ability to analyze an entire population at once, with delivery of outputs at the customer level. This way, marketing can become real-time interaction that is personalized for individual customers, focusing on what marketing really is today – a journey, not a destination.
Collecting such data also allows for predicting the future by finding patterns in data about the past in order to anticipate the future. Utilizing technologies that allow this enables you to pinpoint the offers that might be interesting for a particular customer, and this is widely used by companies such as telecoms that track user behavior in order to make tailor-made offers for individual customers. Advanced customer analytics also use new data types and sources, such as semi structured data from transactional systems, digital properties, IoT connected devices, social media and so on. Including these data types and sources in your analysis is crucial for producing a 360-degree view of the customer.
All quality work requires the right tools for the job, and this type of user experience necessitates advanced analytics tools, such as IBM’s Predictive Customer Intelligence solution which is designed to help you create these personalized, relevant experiences, by enabling optimized and relevant actions at the right time, in the right place. It does so by data mining and modeling to help you anticipate what individual customers are likely to want or do next, and then convert these predictive scores into the most appropriate business action. Details of the process of building brand loyalty and customer engagement with predictive analytics are better explained in the video below:
A crucial element in this is real-time scoring that generates and regenerates predictions on demand so you can immediately react to new information. This makes the customer journey more pleasant, and increases the conversion rates. Furthermore, predictive analytics can be used for cross-campaign optimization, by scanning multiple campaigns and channels, and again identify the most profitable decisions for each customer.
Data driven marketing is not a thing of the future. It’s the thing of our present. And due to the development of data technology and the simplification of processes for obtaining in-depth insights about consumers, companies no longer have the luxury to claim they are not tech savvy.
If you want to learn more about how to make your marketing data driven, then read more.