Artificial intelligence
What does it take to sell a simple pair of running shoes?
Not long ago, the sales trajectory of a pair of sneakers might have included a complex mix of historical sales data analysis, intuition, and market intuition. Sales teams were reviewing spreadsheets from last season, drawing on past shoe sales patterns and outdated customer information to make “strategic” decisions about market segmentation, resource allocation and revenue forecast. Often, teams reacted to shoe trends rather than anticipating them.
But today, AI has fundamentally (and rapidly) changed the business and marketing landscape. Instead of relying on last year’s shoe sales figures or past seasonal trends, AI can now digest large amounts of data (customer reviews, browsing histories, purchasing habits, sentiments on social media, etc.) to anticipate customer retail needs and preferences and forecast sales. with astonishing precision.
Equipped with AI-powered predictive analytics, sales teams can anticipate how many pairs of running shoes will be sold next month, which colors will be most popular, and which marketing messages will resonate best with different audiences.
The wealth of data available on consumer behaviors and purchasing trends makes sales and marketing prime candidates for AI and predictive analytics. Actually, more than half of sales professionals say AI tools are important in their daily roles.
However, as AI transforms sales, long-term success will depend on how companies adapt their data processes and technology infrastructure, according to Amit Sharma, co-founder and CEO of the data connectivity platform . C data.
“AI-based predictive analytics opens up insights that enable teams to make intelligent, proactive decisions,” Sharma said. “But the key ingredient is high-quality data, connected and accessible from a variety of sources. Without a clear picture of the data sets you have, you won’t see the results of the AI initiatives you’re aiming for.
Leveraging AI for Strategic Sales Insights
AI gives sales and marketing teams a strategic advantage to anticipate market movements, consumer behaviors and economic trends in real time. The advantages of the technology are clear: almost three out of four sales professionals say AI can help them extract insights they couldn’t otherwise find, while around two-thirds say AI will help them better understand their customers.
For example, beyond highlighting product trends like fit and color, AI-powered tools can help teams address broader shifts in consumer confidence and expenditure forecasts. A recent Information and analysis Prosper A survey shows that while consumer confidence has improved in recent months – with four in ten consumers confident of a strong economy over the next 6 months – it remains well below pre-pandemic levels.
Thrive – Consumer Confidence Trend
The good news is that consumer spending is expected to increase slightly, with consumers more likely to spend large sums of money on vehicles, vacations and technology purchases. AI excels at identifying these kinds of large-scale trends by connecting the dots in a way that might be difficult for teams to recognize in real time and when dealing with dispersed data sources.
“With AI-powered tools, sales teams can adjust inventory levels and supply chain strategies to meet forecast demand, putting historical data in context with timely macroeconomic insights,” said Jerod Johnson, senior technology evangelist at CData. “At the same time, AI can inform dynamic product price adjustments to attract price-sensitive consumers and locate segments where consumers are willing to spend more.”
Bottom line: For business teams, AI makes it easier to improve data-driven decisions and forecasts, thereby improving strategy development, resource allocation, and identification of revenue-generating opportunities.
Building a Strong Database for AI
Business teams need to be able to easily access, manage and use the complex data needed to power sophisticated AI models. So it’s no surprise that data management is the priority most common technological barrier to successful AI/ML projects today.
“Before sales teams can harness the full power of AI, the right data infrastructure must be in place to ensure data is accurate, contextualized and accessible,” Sharma said. “If your AI systems don’t have quality data, you won’t get quality predictions.”
Additionally, Sharma highlighted three key considerations around data management that can help support sales and other business units as they leverage AI.
- Prioritize data quality
High-quality, accurate and up-to-date data forms the basis of AI-driven analytics. Sales teams cannot afford to “litter” when results directly impact the bottom line. Robust data quality practices and ongoing training are essential to ensure AI models keep pace with fresh data streams from sales, customer interactions, market trends, and more.
Consider forecasting sales trends: an AI model trained on outdated or inaccurate CRM data might fail to capture sudden interest in a new product category or a change in consumer spending behavior, or worse, lead to erroneous and inaccurate predictions. The result could lead to significant missteps in inventory management, marketing strategies and financial planning.
Conversely, when an organization prioritizes data quality management – ensuring data is complete, up-to-date and correctly formatted – teams achieve reliable, accurate predictions and precise insights and actionable tools that allow them to respond to customer trends in real time, and even stay ahead of the trends. curve.
- Organization-wide data integration
Seven in 10 sales professionals predict that most of the software they use will have built-in AI capabilities by 2024. AI-based tools streamline processes and automate mundane, repetitive tasks, allowing business teams to focus on more valuable and meaningful aspects of their work, like engaging customers and driving sales. through personalized, data-driven strategies.
However, if organizations can’t connect disparate data sources across platforms, systems and schemas, business teams won’t gain actionable insights to fuel smarter decision-making.
By connecting diverse and disparate data sources, organizations ensure teams can easily access and use integrated AI tools and advanced data analytics. For example, sales teams can better analyze zero-party data (information that customers willingly share about their preferences and interests) and recommend products that are truly relevant to each customer. Companies like Amazon, Netflix, and Google are setting the benchmark for sales teams using AI to provide highly personalized recommendations.
- Setting high standards for ethics and transparency
The widespread use of AI in customer data analysis has focused attention on how companies collect and use this information. It is up to organizations to build trust and transparency around AI, setting high ethical standards and ensuring open communication with customers about how they leverage and store customer data in their operations.
At the same time, organizations need human oversight and safeguards to moderate outcomes and ensure AI does not perpetuate bias. Without a human understanding of context and cultural nuances, a chatbot trained on faulty data could inadvertently produce offensive content.
