Data and Analytics Can Help Franchisees Stand Out

Lesley French | General Manager Australia and New Zealand | MicroStrategy

Data and Analytics Can Help Franchisees Stand Out

Retailers in the franchise system can face growth challenges in a changing and competitive business market, and franchise managers and store owners must choose the right technology to remain relevant, unique, and attractive to customers. Data and predictive analytics tools are helping retailers across the globe to cater to customers’ specific and changing tastes and boost sales in smart, accessible ways.

Delayed growth can often help disillusion retailers in the franchise system. Attracting and maintaining customers with relevant and enticing products and services while consistently abiding by brand visions, values, and direction is a challenge for franchise retailers.

Personalisation

Customers increasingly appreciate personalised shopping experiences, with 91 per cent of consumers claiming a preference to shop with brands that remember their purchasing habits and preferences. Retailers that offer customers personalised shopping experiences and suggestions aligned to their interests and needs are onto a winning strategy, with studies revealing that customers are more likely to pay higher prices on products during personally catered in-store or online shopping experiences.

Irrelevant and repetitive shopping experiences bore and frustrate consumers and can even drive them away. Customers are looking for options that fall in line with their tastes while also being new and diverse. Ensuring that purchase suggestion are exciting and relevant keeps customers interested and prevents them from straying to other brands or stores for more varied shopping experiences. These fine-tuned sales strategies are proven to keep customers coming back and retailers growing.

Data-driven strategies

Most franchise businesses possess significant amounts of customer data that, if used judiciously, can help them offer customers timely and clever purchase suggestions that adhere to branding requirements. Applying predictive analytics powered by artificial intelligence (AI) to customer data can help retailers predict what types of products their consumers are likely to purchase, when, and at what price.

AI applies computational processes to existing consumer data collected through customer feedback forms, membership details, loyalty and rewards cards, mailing list information, and previous online and in-store purchases. Emerging technologies can analyse this data and provide retailers with essential insights into customers’ upcoming purchasing needs. These technologies can even reveal information, such as customers’ socio-economic backgrounds, locations, and varied demographics to influence successful sales strategies.

AI and predictive analytics can offer franchise businesses of all sizes, significant opportunities for growth. Smaller businesses and retailers should know that data analytics platforms and technologies can be highly accessible and cost-effective, rather than a tool reserved for larger organisations with more resources. AI-powered tools can be an important catalyst in retailers’ growth and success stories.

Retailers engaged in data-driven purchasing experiences are at a critical strategic advantage. Not only can predictive analytics help retailers provide the right products to their consumers, but it can also indicate ahead of time what customers need. In many cases, this means retailers can forecast what customers will want before the customer knows it. This insight can help retailers make evidence-based decisions regarding inventory with confidence. And beyond sales, these products will likely motivate customers to remain loyal to franchises and brands.

Data analysis

Importantly, the more relevant data franchise owners and store managers can collect and analyse, the stronger their insights into consumer shopping habits will be. Data analysis can develop accurate stories about individual customers and customer sub-groups, helping retailers understand the many variables that drive store sales. Elements like location and income are likely to drive purchases. However, the reasons behind customers’ product purchases often occur subconsciously as a result of emotions, impulses, background, and culture. These personal factors carry profound meaning, and machine learning can help retailers connect these details so they can recommend appropriate products and make new sales.

Rewards and loyalty programs that record customers’ purchases build a purchase history with details like home address, age, and job. This data can help retailers identify how much consumers have paid for certain items, and the time of year, month, or the day the transaction took place.

Analysing this data also helps retailers predict other purchase drivers such as when and where customers are going on holidays, activities and hobbies they’re engaged in, and the types of entertainment they consume, and connect this information to realistic price points about customers’ jobs and locations. In this way, retailers are bound to please consumers with relevant products that fall within their budget, ahead of time.

Variables like customers’ ages, careers, gender, and families can also help retailers form an understanding of their various customer segments’ needs, identifying where the highest number of sales and interest lies, and which customer groups will be best catered to by the business. Age or gender groups purchasing fewer items or at lower prices can prompt retailers to enact targeted sales strategies aiming to retain and satisfy, existing customers exhibiting a lack of interest. For retailers in the franchise system, retaining customers is essential for growth.

Value

Predictive analytics and data analysis is highly valuable for retailers catering to a vast range of customer bases. Getting into the minds of customers is increasingly essential for franchise managers and store owners who want to provide better shopping experiences for their customers, which will, in turn, drive sales and business success. And despite the hype and technology jargon, predictive analytics tools can be particularly beneficial, cost-effective, and easy to use for small businesses. Retailers must use the data they already have to generate meaning and accelerate growth.
 

About Lesley French: Lesley French is general manager for Australia and New Zealand (ANZ) at MicroStrategy. She leads the company’s local team to drive growth across the ANZ market, manage sales and alliances, marketing and business development, and more. Lesley also works to strengthen MicroStrategy’s local executive customer relationships.

About MicroStrategy: MicroStrategy is a leading worldwide provider of enterprise analytics and mobility software and services. MicroStrategy 2019 delivers modern analytics on an open, comprehensive enterprise platform designed to drive business results with Federated Analytics, Transformational Mobility, and HyperIntelligence.