Artificial intelligence, or AI, has quickly infiltrated our daily lives, helping us do more, think strategically, and uncover new possibilities. In business, AI can unlock thousands of data points into actionable, accessible insights that drive strategic growth. Learn how analytics powered by AI can transform data into actions that anticipate demand, improve customer retention, and accelerate growth.
1. Anticipate Demands to Inform the Business
Your marketing team may be comprised of some of the most intelligent and capable people in their field. However, organizations can become too complacent with their industry to make accurate predictions without certain tech tools.
Predictive analytics gives teams the ability to forecast what customers will want, not just what they think they need. AI can quickly analyze historical purchases, web engagement, and customer behavior, identifying patterns and shifts in consumer behavior. AI has the advantage of computing speed and mathematical precision well before monthly metrics decks are complete.
If customers adjust their shopping habits from daytime to evening, that may indicate certain changes. If browsing habits start to include purchases for the family, a retailer can adjust campaigns to fit the search criteria.
One example of the most powerful uses of predictive analytics is when exploring AI in automotive industry sales processes. Shopping habits change quickly, and the speed of sales responses is essential for success.
An automotive dealership using predictive analytics can forecast which customers are likely to be ready to buy. Customers using the dealership’s service department can analyze the customer’s service history to predict buying patterns. Online browsing for car repair advice can provide an opening to propose a new vehicle versus costly maintenance and repairs.
2. Personalize Experiences Across Channels
When someone starts looking into baby gear, that customer can be tagged to a new parent category. With AI insights, this retailer can appropriately propose products and content relevant to the shoppers’ life stage.
Similarly, customers’ browsing history can be compiled to inform the type of vehicles they may be interested in. Paired with confirmed customer data like age, occupation, and marital status, car dealerships can position the most relevant marketing campaigns.
A retail brand can use predictive analytics to forecast demand for seasonal products and adjust campaigns and inventory to match. Seasonal changes can inspire winter weather prep, maintenance, and repairs. Using prior year sales data and weather predictions, companies can warehouse the right stock of snow tires.
AI can also help brands understand which channels have been most effective for their specific customer base and personas. These insights can help marketers be more efficient across platforms and invest in the highest-performing categories.
3. Invest in High-Value Leads to Maximize Conversion Rates
Billboards are great at exposing your offer to a large audience. However, broad campaigns can fail due to their lack of targeting. Predictive analytics can help sales teams focus on what matters most – the highest converting leads.
AI-powered scoring models help analyze buying patterns and sales timelines from previous successes. By blending data from previous deals, customer resource management (CRM) data, and web behavior, teams know where to focus efforts. Blending the data gives a score to each customer, which helps sales associates know which customer to target first.
AI also anticipates future purchases and informs nurture strategies that long-term sales retention and marketing campaigns can be built upon. An automotive dealer can keep a customer engaged with service offers, trade-in opportunities, and upgrade pitches. By establishing a nurture strategy, this dealer can earn customer trust and loyalty and cinch the next sale.
4. Predict Customer Experience Issues Before They Impact Retention
Businesses can identify the early warning signs of customer churn by monitoring customer behavior through AI. Brands using predictive analytics to tabulate customer behavior and norms can trigger corrective action when risks are detected.
Retain the loyalty you’ve earned with retention strategies that flag risk areas and address them right away. These models can identify changes in social engagement, purchase frequency shifts, and negative sentiment.
Marketing and customer care teams can develop corrective action plans based on prior successes in improving retention. With this investment, organizations can improve customer loyalty and achieve a higher customer lifetime value with predictive analytics.
5. Accelerate Growth and Strategic Investments with Data-Driven Insights
Your product development team likely has a wealth of ideas for what’s next. However, what matters most is anticipating customer needs before the competition. Predictive analytics can unlock product and service needs with enough time to develop, test, and launch them into the marketplace.
Brands can understand what categories to expand into and when product lineups need an upgrade. A product launch may include a baseline offering, but future iterations can feature upgrades based on customer feedback and testing.
A new snack food line may launch with a flagship product, but through customer insights, new flavors and packaging follow. Brands can anticipate seasonal shifts, devise smart manufacturing plans, and manage warehousing and hiring needs with AI. With better data and forward-looking insights, companies can invest wisely, grow strategically, and delight customers better than ever.
See the Future and Be on the Cutting Edge
Predictive analytics powered by AI doesn’t just help your business understand the past – it unlocks the future. Organizations and leaders who embrace AI and invest time to understand it can make smarter decisions sooner. With meticulously calculated datasets, leaders can confidently make decisions and embark on new growth strategies backed by AI-powered predictive analytics.
