Cayleigh Zambonini | Account Director | Boomtown | mail me |
Often considered a buzzword, big data can be described as the vast volume of structured and unstructured data which floods business daily. However, it is not the size of the data collection that counts, but rather what you choose to do with it that counts.
So, one could deduce that in-depth analysis of big data could lead to improved decision making and strategy? The answer simply put is no, but it does have the potential to do so.
Combining integrated marketing management strategy
By combining integrated, marketing management strategy and big data, marketing agencies can create a significant impact on:
- Client engagement – Apart from being able to tell who your customers are, big data can assist in understanding customers’ location and peak visitation periods, what they want and how they want to engage with you.
- Client retention and loyalty – In a modern world where options are aplenty, brand loyalty is king for a sustainable and scalable business. Big data allows for derived insight into what influences client loyalty as well as what promotes repeat purchases.
- Marketing performance and optimisation – Time is money, therefore efficiency is king in the digital landscape. Big data allows companies to deduce the optimum marketing spend through numerous channels while continuously improving campaigns and strategy through testing, measurement and analysis.
Types of big data in marketing
The three primary categorical types of big data in marketing are:
- Client or customer data – When looking at customer or client data in a marketing context key data includes behavioural, attitudinal and transactional metrics from primary sources as campaigns, websites, point of sale, consumer surveys, social media, online communities and loyalty programmes.
- Operational data – Operational data characteristically comprises of impartial metrics that quantify the integrity of marketing processes relating to – marketing operations, resource allocation, asset management and budget.
- Financial data – Usually contained in companies’ financial systems, this big data category may include sales, revenue, profits and other objective data types that measure the financial health of the company.
After identifying the positives of big data, it is also important to note the challenges and best practices related to marketing activities. The root cause of big data challenges arises from analytics systems not being aligned to the company’s data, processes and decisions.
The three primary challenges are:
- Knowledge of what data to collect – Collect the right data for the purpose as opposed to the quantity.
- Using the correct analysis tools – Analytical user tools assist with the aggregation and analyses on data to derive key insights; this leads to the next challenge.
- The capability of going from data to insight to impact – It sounds simple enough, but this is why data scientists and analysts are in high demand. It’s the ability to take the raw data, organise it, analyse it, derive key insights and then use this to inform decision making.
Like it or not, big data is a crucial element in the future of marketing and media, so keep the following in mind for best results:
- Digging into big data can reveal further insights – Imagine a company’s data as an onion, with true big data analysis you can peel away layers and delve deeper into your results for richer insights. Like an onion, these results can be further analysed revealing new layers each time.
- Be sure insights are shared to people on the ground – It’s great to collect critical insights from big data and look impressive in a board room presentation, but it’s essential that these insights are shared with the people involved in company operations so they can understand the thinking behind new decisions and possibly add valuable input.
- Take small steps before you leap – The sheer volume of big data can seem overwhelming. Therefore it’s best to begin by concentrating on fewer significant objectives. It’s always important to start with asking “What is the desired outcome?” Once that is known, it becomes possible to identify the data you require.
Once that is completed, progress on to your next objective and peel away at those layers.