How to Build Your Customer Base Through Loyalty Programs
Nowadays, businesses may be converting phone scrollers to new customers and hooking them into enticing adverts.
However, it's the loyal customers who are your biggest fans. Studies show they bring in a whopping 65% of a retailer's business! And with a smart loyalty program, that number jumps by another 12-18%. F&B businesses should therefore focus on building relationships with these customers, not just as a transaction.
How? By analysing their purchases, understanding their needs, and crafting relevant offers, and sprinkling in sweet incentives. This creates a delightful experience, that attracts not just return customers, but new ones too!
Kate Muslayah, Founder & Director of eCommerce Me, shares her in-depth insights on what keeps customers engaged and ultimately, devoted to the brand through loyalty programs in areas such as personalisation and segmentation as well as multi-tiered rewards systems that can incentivise them further. With the emergence of data and AI, she sees their integral role in personalised marketing and insights-driven decision-making and urges us to take precautionary steps in mitigating the risks of homogeneity in AI.
What are the important areas to consider when designing a holistic & effective loyalty program for omnichannel setting?
To effectively manage a situation where a food and beverage company has data but is not utilising it properly, follow these key points:
- Recognize the value: Understand that data can provide valuable insights and improve decision-making and create use-cases to showcase the value it brings.
- Assess data availability: Determine what data is being collected, where it is stored, and how accessible it is within the company and who needs what parts to help drive objectives and continuous improvement.
- Break down data silos: Encourage collaboration between departments and integrate data systems to get a holistic view of the data.
- Invest in analytics tools: Use tools that can extract meaningful insights from the data, ranging from simple spreadsheets to advanced analytics software.
- Train and empower employees: Provide training to enhance data literacy skills, enabling employees to understand and interpret the data effectively or get help to connect your disparate data together from partners.
By following these steps, a company can optimise the use of their data, make informed decisions, and improve overall efficiency in the food and beverage industry.
How do you manage the situation that the company has the data, but not utilize it properly?
When the company has the data, but do not utilise it well – with siloed data that are difficult to access or not leveraging on analytics to convert data into actionable insights and look at new relationships, it can cause inefficiencies in optimising them. This is a very common problem.
AI and GPT uses and retains information. This creates a risk of homogeneity. How do you prevent this loss of competitive advantage and trade secrets?
How do you create a loyalty programme of consumers who do not just go after freebies and discounts, but genuinely appreciate your services and products?
Everyone likes freebies and discounts. That is human nature.
The first thing to do is understand your customers and their reason for buying as well as all your data and insights on consumers and your sales journeys. Analyse the value equation by assessing if they are costing less to acquire or retain compared to other customers. Determine if their potential switch is influenced solely by price or if there are other factors that contribute to their loyalty and their lifetime value. It might mean you have a higher lifetime value of a customer if you offer them discounts and freebies.
Other options you can look at is:
Exclusive benefits - i.e. new products that generate reviews or UGS, Trade ups, offers for increased basket value or to trial something new, personalisation to suit unique needs of shopper, creating a sense of community that they feel they belong and contribute to, acting on feedback and acknowledging this. However, in many industries, including F&B, consumers tend to have a repertoire of brands they shop from in most categories and your role it to ensure they purchase from your brand most of the time, and ideally convert them from other brands with compelling offers.
When data is collected in self-declarative manners (e.g., income and other socio demographic data), how reliable are they in your experience?
In my experience, data collected in this way can be moderately reliable but very likely subject to biases and inaccuracies. Making it broad multiple choice gives people more options and they feel they are not being pigeonholed as much. Cross referencing with fact-based data and other sources where possible means you can overlay other facts to determine the level of accuracy and more complete picture.
Providing a reason for people to answer accurately that benefits them delivers even better accuracy. Also, often it is used to create personalisation when no other tools exist and it does not need to be 100% accurate, as it is informing macro decisions you are making around promotions, media, marketing and therefore as long as its reasonably accurate the outcome should be the same.
Determining if you need a high level of accuracy and for what purpose will help with this, as will investing in personalisation tools that start to understand your consumers and what their preferences are to enable the right engagement strategies.
