SME’s & Big Data — Why It Matters

I unpacked big data and how marketers use it in today’s technology-driven world in my previous post. Today, we will look at how big data affects small to medium enterprises (SME’s) and why it is becoming so important when developing your marketing strategy looking into the future.

Big data’s value is measured by what you do with it, not by how much data you have. Smartphones, tablets, gaming consoles, and pretty much any programme, service, or platform accessible via these devices keep digital consumers connected at all times. They build various consumer touch-points through different mediums as they switch between devices and platforms — web, offline, proprietary, third-party, corporate networks, social networks, location-based, and mobile. This knowledge provides marketers with an excellent opportunity to better target their customers.

Marketers can mine, merge, and analyse all data forms near real-time using big data technology and analytics methods. This will help them identify hidden trends, such as how different types of consumers communicate and how this influences buying decisions. Companies may then establish tailored marketing strategies that appeal to the customer’s individual needs using these insights.

Big Marketing Results from Big Data

Several big data applications in the customer service domain are demonstrating the enormous potential for driving marketing effects:

1. Next best action (NBA) to engage consumers. NBA marketing is a real-time customer-centric marketing approach that assesses all potential deals for each customer. The next best action bid is determined by the customer’s wishes and needs and the branding organisation’s corporate goals, plans, and regulations. Real-time decision-making technology uses data from call centres, transactions, customer details, and a set of business rules to determine one of many offers the customer is eligible for at the time of interaction.

2. Online shopping that is personalised to the customer. Two decades ago, the retail industry was transformed by the advent of online retailers who used the Internet to widen their business reach. It now brings shopping to the next level by turning it into a personalised experience based on vast amounts of data collected and processed. These data sources developed by several terabytes every day are transformed into information and insights by intelligent machine-learning algorithms. This information is then used to give customers a more personalised experience by highlighting items of interest, recommending the best prices, and finding what they want.

3. Using big data to create targeted dynamic advertising. Data monetisation allows enterprises with massive volumes of data to use their previously untapped data or underutilised data. Several powers are merging to construct optimal data monetisation conditions. New types of information, such as exact, real-time geolocation data, are being encouraged by mobile devices.

4. Product life-cycle management using machine-to-machine (M2M) analytics. Sensor technology has progressed rapidly in computers, vehicles, mobile devices, power grids, and business networks. Data from machines to machines (M2M) is produced at an unprecedented rate and in real-time. Data can provide feedback for software developers, customer service representatives, and sales representatives, improving product functionality, increasing revenue, and cutting costs.

A practical application is to monitor consumer use of devices and goods and send proactive reminders and triggers to the sales team when it’s time to notify the customer about a product update or refresh. This can be a very successful way to establish a one-on-one relationship with a client and assist with cross-selling and up-selling.

Beginning Your Big Data Journey

Companies must understand how big data moves between various sites, outlets, systems, owners, and users before putting big data to work for them. To take control of this vast “data fabric,” there are five main steps to follow:

1. Come up with a big data plan: A big data strategy, at its most basic level, is a plan that aims to help you handle and enhance the way you collect, store, manage, distribute, and use data both within and outside your business. In the face of an abundance of data, a big data approach sets the foundation for business success. It’s essential to think about the current — and potential — business and technology priorities and strategies while designing a plan. This necessitates treating big data as a valuable business tool rather than a byproduct of applications.

2. Recognise the various sources of big data:

• Wearables, smart cars, medical devices, industrial equipment, and other connected devices send streaming data to IT systems via the Internet of Things (IoT) and other connected devices. You will evaluate this big data as it comes in, determining which data to hold and which to discard, as well as which data requires further investigation.
• Interactions on Facebook, YouTube, Instagram, and other social networking platforms generate social media data. This involves large quantities of big data in the form of photos, videos, speech, text, and sound — all of which can be useful for marketing, sales, and customer care. This data is often in unstructured or semi-structured formats, and it presents a particular consumption and analysis challenge.
• Large quantities of open data outlets provide publicly accessible data.
• Data lakes, cloud data sources, vendors, and customers can all provide additional big data.

3. Big data access, management, and storage: Modern computing systems have the speed, resources, and versatility needed to access large quantities quickly and types of big data. Companies often include methods for integrating data, ensuring data integrity, providing data governance and storage, and preparing data for analytics, in addition to secure access. Businesses can store data on-premises in a conventional data warehouse, but cloud solutions and data lakes provide low-cost alternatives for storing and managing big data.

4. Analyse vast volumes of data: Organisations may use all of their big data for analyses using high-performance technology like grid computing or in-memory analytics. Another method is to decide which data is essential ahead of time and then analyse it. Big data analytics, in any case, is how businesses extract value and knowledge from data. Big data is constantly being used to fuel advanced analytics initiatives, including artificial intelligence.

5. Make data-driven, intelligent decisions: Data that is well-managed and trustworthy leads to trustworthy analytics and decisions. To remain competitive, companies must leverage big data’s full potential and act in a data-driven manner, relying on information provided by big data rather than gut instinct to make decisions. The advantages of data-driven decision-making are apparent. Organisations that are data-driven perform better, have more predictable processes and are more efficient.

Bear in Mind When Taking a Big Data Journey

Big data analytics is a journey that assists companies in resolving critical business challenges by turning data into knowledge that marketers can use to influence business decisions and drive critical business outcomes. Companies should not be intimidated by the myriad obstacles that could await them when they attempt to capitalise on the big data opportunity.

Big data benefits are many, and they will be primarily dependent on the organisation’s senior leaders’ vision and leadership. Senior leaders can promote innovation and learning by using big data to drive critical pilots and touchpoints. These small successes would pave the way for large-scale big data marketing methods, making them more creative and impactful to consumers based on the small victories.