Data has become a direct fuel for the growth and success of any business. Brands can use various data analytic techniques to improve efficiency, grow, and stand out. Predictive analytics is one of the most advanced of them.
According to VentureBeat Report, 44% of companies have completely integrated AI-powered predictive analytics into their marketing strategy.
And 60% of retailers can increase their profit margin using predictive analytics.
Predictive analytics can help brands make better decisions by providing insights. It can make it easier to identify customers' expectations and buying patterns. Let’s focus on a small story that can help you understand better.
On Sunday evening, you decide to visit a restaurant with your family. While scrolling through the menu, you’re confused with several unique dishes, and a waiter approaches you to ask for the order. You asked the waiter to recommend some dishes per your taste, and the waiter took out a device to analyze and recommend personalized dishes.
The system instantly entered their database, sorting out dishes matching your preferences. Within seconds, the Waiter represented you with the dishes that perfectly matched your taste and diet.
You enjoyed the delicious dinner and shared the experience on social media. How will you feel when you get a recommendation that looks personalized?
That’s how predictive analytics work, collecting your data from several sources and preferences to provide a personalized experience.
In this blog, we shall understand the role of Predictive Analytics in brand strategy. But before that, let’s dive deep into its meaning.
Predictive analytics is processing large amounts of data to make future predictions of trends and customer behaviors and optimize your brand strategy accordingly.
It helps brands to find patterns within data sets, identify risks, and make informed decisions. That way, you can stay ahead of the competition by giving them an edge, understanding customers, and increasing sales.
Here are some benefits of Predictive Analytics-
Predictive analytics helps brands to forecast product demand in a particular area using historical data. With the forecasted data, brands can efficiently improve supply chain management by sourcing required raw materials, optimizing inventory levels, and streamlining logistics.
In addition, the data can be used to allocate resources efficiently, improving operational efficiency, increasing profitability, and streamlining operations.
That helps a brand to stand ahead of competitors and enhance customer satisfaction.
Knowing what your customers want and what is trending in the market can help you provide personalized offerings, enable customization, and leave a lasting impression on your customers.
The data-driven predictions can also help you create offers and products that create a win-win situation for both customers and your brand. In addition, you can identify the root causes of customer dissatisfaction that can help to improve overall customer experience.
Predictive analytics leverages past and present data to predict what is likely to happen, detect frauds, and what proactive steps can be taken to mitigate those risks.
Here are different types of risk that prediction analytics can help manage:
Predictive analytics can help you dive deep into customer data and identify trends in user behavior. You can understand how customers behave with your product, analyzing it with the customer journey. That can help you understand customers' needs and wants.
The level of personalization is directly related to the CTR and campaign performance. Crafting high-performance creatives for each campaign and audience using real-time consumer data can help you deliver personalized campaigns.
Let’s now talk about how prediction analytics can help improve brand strategy.
By leveraging data and other crucial details, brands can improvise their brand strategy and maximize their profits. Here are some ways predictive analytics can help in brand strategy-
Predictive analytics can analyze different customer patterns like buying patterns, preferences, and trends that can help brands understand customers in more depth.
That can help to create a more holistic view of customers and put efforts into retaining the customers over time. For example, Stitch Fix uses “Style Shuffle,” a quick way to collect feedback on items and outfits. The real-time data helps them to provide personalized customer experience and collect insights into macro trends.
One mistake brands make is they analyze the market before launching any new product or building one. But, as the market trends change or shift, they fail to serve the customers' needs.
Predictive analytics can help analyze real-time data like consumer demographics, trends, and purchasing behavior; it predicts how many consumers are likely to purchase your product.
For example, Amazon uses big data gathered from customers while they browse to build a recommendation engine.
Customers work hard to acquire new customers through marketing and branding. You invested a lot of time to build trust among them, and holding them makes sense. Customer retention is turning customers into repeat buyers and preventing them from buying through your competitors.
Predictive analytics can analyze previous purchases, viewed products, and abandoned carts to create a unique customer experience. In addition, you can also leverage it to forecast a potential issue and solve it proactively before customers complain about it.
Jumbo, a leading lottery solution provider, uses algorithms to learn consumers' past purchasing behavior and identify what customers want to buy next. The more data they have, the more sales they make when the customer returns.
Predictive analytics can help to manage big data efficiently to optimize overall supply chain operations. By analyzing past trends, brands can get an idea of which products or services will be in high demand in the future.
That can help to improve supply chain processes like inventory management, warehouse optimization, supplier management routes, and more.
In addition, it can analyze important supply chain metrics that can be used to track performance and identify areas of improvement. For example, it can identify trends in delivery times, inventory levels, and production output.
An example from IBM is that it uses Watson to make weather condition predictions that impact customer experience., They can make informed decisions on the supply chain by analyzing the data from different platforms.
Predictive analytics is one of the crucial tools that can help in every aspect of brand strategy, from marketing to customer retention and building solid branding. Tapping and utilizing these benefits can help you strengthen your strategies and provide sustainable growth.
Is your brand utilizing Predictive Analytics to grow?
