Data is of paramount importance to brands across the globe. Known for its excellent customer experience, the secret ingredient in Starbucks’ success rate is the way the brand handles data.
Starbucks leadership team includes a head of Global Strategy, Insights and Analytics, which has shaped the brand’s image across the globe, using “methodologies ranging from ethnography to big data analytics… that helps support Starbucks pricing strategy, real estate development planning, product development, trade promotion optimisation and marketing strategy.”
The iconic brand uses data analytics to drive business decisions, such as:
- Real estate: With the help of location-analytics company Esri, Starbucks analyses maps and retails locations to determine new store locations, using data such as population density, average incomes, and traffic patterns.
- Menu design and optimisation: Starbucks uses data to align its menu and product lines with consumer preferences based on what works and what doesn’t. For example, to introduce its grocery line of k-cups and bottled beverages, Starbucks used both data from its stores as well as customer market research to decide which products to create.
- Personalisation: More than 14 million people have signed up for Starbucks Rewards loyalty program. The brand analyses what loyalty customers are ordering and how often they order it, to make personalised offers to them, thereby driving sales.
Organisations worldwide can replicate Starbucks’ success model, by merely working on their data and extracting enough insights from it to drive sales.
To solve data productivity issues, marketers and brand must apply a practical data management route. Making relevant, substantially analysed, and timely data can create a massive opportunity for businesses to make improved decisions for operations, thus aiding its growth.
Also Read: CDP or DMP, Who Rules Customer Data Management?
We share some basic best practices for data management that can help drive data-driven decisions for any brand’s marketing strategy.
1. What Are Your Business Goals?
It is easy to lose oneself in the hordes of data available and subsequently forget why are we analysing it. Is it consumer behaviour? Buying patterns? Competition research? Improved operations? Or something else? One key rule is to set clear business goals and also keep them in focus throughout the planning process to determine which datasets hold the most important information and whether or not they need to be placed in the data management silos.
With clear business goals, brands will have better clarity about their data, avoid which data to keep and which to let go. In this way, marketers can avoid storing too much data which they’ll have to sift through later.
So best practice one can be recapped as – Always start with your goal and then decide what data and data technologies you need to achieve that goal.
2. Grow With Latest Technologies
Latest technologies like artificial intelligence (AI) and machine learning, can help quicken the process of analysing large datasets and compiling a report based on it while being able to draw much deeper learnings.
Keeping the GDPR legislation in mind, most companies dealing with large amounts of data will have to look at AI to meet the compliance needs. The GDPR rule requires companies to give consumers the option to opt-in and -out of communications, as well as be able to provide details of the data being collected, which will be made possible through AI-based data management systems.
Also Read: One Year of GDPR: Privacy Laws, Data Breaches, and the Impact of Regulations
3. Data Access for the Right People
The team analysing the data collected by the organisation is of the utmost importance while managing data. When you have the right people in charge of your data, then half the job is done. When migrating to data management and data analytics, the most natural thing for an organisation to do is make the accounts or IT team in charge of the analytics. However, the scenario with data management is more complicated than handling data spreadsheets. So either the choice is to hire data experts or train your existing staff in data analytics and data management systems.
Also, it is important to let the right people have access to the right data. Has it ever happened to you that you stored something away so carefully that you couldn’t find it again when you needed it? It occurs in data management, as well. Certain data is stored too securely, and the teams looking for it might not be able to access it while analysing. When storing data, it is essential to provide ease-of-access for those who need to use it, while also negating access to those who do not have the correct clearance. In a data-driven culture, it is crucial to invest efforts in data security as well as ease of access.
4. Cyber Threat and Response Planning
Data-breach has become a household term now. A latest IBM and Ponemon Institute study found that organisations in the Middle East reported the highest average number of breached records with nearly 40,000 breached records per incident (compared to the global average of around 25,500.)
Organisations have to be prepared with a response plan in the event of a data-theft or security breach. Rather than reaction, when one is prepared with a pre-established response plan then the decisions to be taken will be within the periphery of whether legal action is required to contain, eradicate, recover or clean-up the threat.
By having a plan with clear decision points, companies will know if they are required by law, regulation, or good faith to disclose a potential or realised breach.
Also Read: Understanding and Influencing Customer Behaviour Using Customer Data
5. Keep Data Clean and Classified
Lastly, take ownership to keep all your data clean and classified. Overlooking this process can be the cause of a lot of data management issues. So stay on top of all your data processes, make sure you make someone within the organisation accountable for data classification and process. What is the quality of data being collected? Are you over-collecting data that results in over-bloated servers and spiralling costs? Investing in a person who can overlook these vital details will result in best data practices and in return, better analytics for your business.
Make data a part of your company’s culture, instead of just one data-team slogging it off in one department. The upkeep of data management practices and principles is a task that should be undertaken by the entire business. It will enable a company to define the policies and goals of its business across all departments.
Following these data management best practices should help your organisation to implement an affirmative data management strategy in your business. It will lead to better analytics and significant potential for growth of your business.
