Predictive analytics is the process of analysing historical and current data and applying advanced statistical methods and analytical tools to make reliable predictions about the future. In a business context, predictive analytics often involves the creation of a predictive model used to exploit patterns in historical financial information, customer data, and other third-party data sources to identify risks and opportunities.
In most applications, predictive analytics involves first identifying a business problem and identifying what kind of data is needed to help solve the issue. Next, data must be collected and prepared for analysis. Once the data is prepared, the analyst will use a variety of data mining, machine learning, and statistical techniques to uncover numerical patterns and relationships between variables. This information is then used to create a model that can be used to make reliable predictions.
WHAT AMAZON AND NETFLIX KNOW ABOUT US
The most extensive use of predictive analytics is in e-commerce. It is primarily used for predicting what visitors want to buy based on their demographic and psychographic profiles. Using the combination of visitor information with their browsing and purchase behaviour, companies are able to increase engagement by delivering highly personalized, highly targeted content and advertisements.
Amazon’s growth is fuelled by their ability to use data to build a complete picture of their online visitors and then predict customer purchase. It has seamlessly integrated its predictive technology into every aspect of the purchasing process, tracking visitors’ behaviour from the moment they log in, in order to make effective recommendations for current and future purchases.
Netflix, another forerunner of personalised product recommendation, utilises predictive analytics to deliver highly relevant content to users. Netflix gathers data on users’ preferred genre, ratings given, views, surveys, and other actions to predict and recommend movies and shows. According to the company, 75% of what people watch on its platform can be traced to its personalization efforts.
BOOKING A HOLIDAY AT THE BEST PRICE
The travel industry is notoriously competitive, with volatile peaks and troughs in demand and many low-margin routes. This can leave travellers in the dark, unsure of the best time to book. Sometimes it’s better to book ahead, at other times it’s better to wait until closer to the date of departure.
Travel app Hopper stays one step ahead by predicting future pricing patterns and alerting travellers of the cheapest times to buy flights to their preferred destinations. It does this by watching billions of prices every day and, based on historical data for each route, anticipating how the trend will develop. Users can then set up notifications to remind them to book when these price drops happen.
OPTIMISE MARKETING STRATEGIES
Knowing where to spend your advertising budget is essential, but so is knowing where not to spend it. Predictive analytics allows companies like Under Armour to hone in on the areas that will deliver the greatest returns, and reinvest budget that would otherwise have been spent inaccurately.
Artificial intelligence is used by Under Armour to perform tasks such as sentiment analysis and social listening to understand what customers think of the brand, and where the gaps in the market are. This has led the company to focus on becoming a digital fitness brand, an initiative that has seen it carve it a new niche in a saturated market. Under Armour produces physical fitness products, but also apps and wearable devices to tie the offline and digital worlds together. The more people use the products, the more data Under Armour can gather to improve its offering.
Predictive analytics can help you not only attract new business, but also help you retain the customers you attract and convert your first-time sales into recurring revenue.
To apply predictive analytics for customer retention, companies first need to collect data on their customers, including details about the products and services a customer purchased (e.g. price, brand), demographic and geographic information, and whether they are first-time or returning customers. By applying predictive analytics to this data, you can produce a ‘score’ that indicates how likely a customer is to make additional purchases. If you know which customers are more likely to return, you can target them with personalised marketing campaigns, like special rebate and discount offers or specific product or service recommendations. Providing personalised messages can help foster loyalty and keep customers away from your competitors.
Employee turnover creates significant direct and indirect costs related to training, lost knowledge, and decreased productivity. As such, improving retention is a primary objective for many HR departments. Through predictive analytics, companies can identify which employees are most likely to leave, allowing them to proactively ensure that those who are most valuable are satisfied and appropriately incentivised. HR departments already have much of the data required to apply predictive analytics in this context, including level of pay, tenure, performance level, attendance records, and history of promotion. Some companies even incorporate socio-demographic information and employee commute time data into their predictive models.
Predictive analysis can also empower many public sector organisations, from assazisting hospitals in predicting numbers of returning patients to guiding mass transport operators on traffic patterns and passenger volumes, along with helping utility companies to better prepare for surges in demand.
Having consulted a number of businesses on the implementation of predictive analysis, our firm has witnessed first-hand how any voluminous amount of structured or unstructured data could unfold in exciting ways and directly impact our lives, making effective use of precision technologies. Such precision technologies give us incredible insights into the process and decision-making ideologies, ultimately resulting in a massive increase in productivity with a drastic cost reduction.