Using big data in marketing and ecommerce

“Big data” has been used a lot. But how do you use big data for marketing in ecommerce stores?

If you are amassing a ton of records that create big data sets in your enterprise business, you have the power to harness the potential of using big data in marketing and ecommerce. Big data is opening a new world of possibilities for marketers in the digital environment.

 

Discover how analytics are not just changing, but revolutionising marketing as we know it

Before e-commerce and the rise of online shopping, marketers only had a basic set of sales data to go by. Marketing campaigns were created, and changes in sales were monitored. Marketing teams could not determine with any certainty what was happening when people saw the advertisements. Population statistics, census data, and focus group results were used to predict marketing outcomes.

Market research gave marketers the opportunity to hear the thoughts of their potential customers, yet the feedback that was gathered was often hypothetical. Asking someone if they would buy your product is much less certain than having a specific record about who has just bought your product.

Another early marketing enhancement was the introduction of the loyalty card, which customers would swipe in-store to build loyalty points. Loyalty cards enabled marketers to see data about how often shoppers were shopping and how much they were spending when they did. This type of market research was prone to bias and was frequently inaccurate or skewed.

 

 

 

The shift from structured data to big data

Traditional analytics focus on structured data, which is organised and easily searchable. In contrast, big data refers to large and complex data sets that are difficult to manage with traditional data processing tools.

As shopping moved increasingly online, it became much easier to track consumer behaviour more accurately. User data related to searching, browsing, shopping, and providing customer service can all be captured and analysed. This data can become hugely detailed within big data models and can help you with insights like:

• How many products has a user viewed?
• How long have they viewed a product?
• How many reviews have they accessed?
• How many brand comparisons have they conducted?

Online advertising is also much more targeted. When a company paid for a billboard or a TV advertising campaign, they had no control over who saw the advertisement. The advertisements were placed where it was hoped they would be more likely to be seen by a target audience. However, there was no guarantee. Online marketing enables advertising messages to reach highly segmented market audiences directly.

 

Big data marketing – volume, velocity, variety, and veracity

Big data enables marketers to collect a vast range of information, preferences and behaviour tracking. The most insight can be gained from the gap between interest and purchase.

Smarter and a wider variety of sources generate customer data at a rapid rate, which must, in turn, be processed at high velocity. Data processing tools enable marketers to see the bigger picture of data, and there is so much data to be accessed.

Many Marketers need stream computing to deal with big data in high volume or from various sources. Stream computing for marketers involves processing and analysing real-time data streams to gain immediate insights and take rapid action.

Stream computing provides insights delivered through a continuous query. Data is constantly collected and observed to enable you to monitor customer sentiment, reactions, mood, and opinions.

This technology allows marketers to make data-driven decisions on the fly, enabling personalised and timely customer interactions. You can identify trends, detect anomalies, and optimise marketing campaigns in real time by continuously processing and analysing data from various sources such as social media, online stores, and websites. Streamed data can compare information from a multitude of sources and across several marketing campaigns to:

• Identify and marry up data streams related to customers and events and demonstrate specific customer intent under certain conditions
• Create immediate attributions on a customer record, for example, when items are left in a cart or when a customer has conducted multiple views of a product or page
• Score streaming data for decision making, for example, to deliver on internal decision trees, like emailing an offer when a particular chain of web browsing events has occurred

Streaming can cause big data difficulties, primarily in volume. The sheer amount of data generated can be overwhelming, requiring the use of massively parallel platforms to handle some of this pressure. These platforms are computing systems that consist of many processing units working together to execute multiple tasks simultaneously.

 

Customer relationship management through big data

Marketing also involves customer relationship management, as marketers rely more and more on customer touchpoints, emails, social media contacts, and web chat records to monitor and assess customer intent, brand loyalty, and levels of engagement. Companies now record phone conversations, convert them to text, and turn them into the data mix.

The language used in emails and web chat can reveal a great deal about how a customer feels about your company. When data analysis shows a high frequency of words like “disappointed” or “frustrated,” you might be picking up on the beginning of a worrying trend for your business and conveying positive or negative sentiments about not only their opinions of your products but also about their experiences interacting with you as well.

 

Improved collaboration

Another thing about big data is that it can bring businesses together. Marketing teams typically occupy a role separate from the main business, often working in silos and not connected to the day to day activity of the business.

Big data gives everyone access to information that can enable innovation and improvements. Sales data, reviews, refunds – big data records need to be returned to product development and design teams, providing incredible insight into customer experience. Are T-shirts being returned because they are prone to shrinking? Poor reviews about a product? These kinds of observations are not just for the marketing team to handle. Analytics and assessment of the holistic product lifecycle create ongoing feedback systems. The marketing and media team may have access to a much more comprehensive range of customer transaction and metrics, and need to be able to communicate this information effectively around the organisation.

ERPs have often been deployed as a strategy for unifying different teams. By tracking information across various internal systems, companies can get a more detailed view of operations. ERPs enable the comparison of data housed in different systems, demonstrating how centralised data can work for everyone.

 

Big data handling with Master Data Management

MDM systems have achieved more holistic customer data integration, which compares and matches user profiles across multiple data sources. MDMs collate customer records and present you with unique customer profiles that have been created from any number of disparate software and apps. MDM tools can also generate content hierarchies to standardise data across systems to be consistent and accessible to interrogate. Pimcore also enables the cross-channel automation of marketing messages across several existing channels.

Pimcore is an MDM solution that can also be used as your Customer Data Platform (CDP). Combining your MDM with a CDP reduce the need to run two solutions concerned with different activities. Bring product management into line with Pimcore. Discover the power of Pimcore with Stimulus.

Our team of experienced developers and marketing experts can deliver you business efficiencies with Pimcore..

 

 

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Related Questions

What does the future hold for marketing roles?

The knowledge and experience that marketers need is changing, and with new technologies, marketing needs to be understood in new ways. Marketers should no longer refrain from coming up with a campaign and take an educated guess about where to place it for the best results. Modern-day marketers must manage messaging across cloud-based data sources, apps, and social media.

Marketing organisations and departments are faced with significant changes to how they work. Ultimately, there is a much higher degree of interaction between your business and your customers.

Modern-day marketers must know where to find and how to use big data, using interactions and observations to build personalised and effective relationships with customers and market segments. Marketing roles today may be Marketing Analysts or Customer Experience Coordinator, and Social Media Manager. They are managing and monitoring brand sentiment and customer engagement. On top of this, they must find and deal with information, manage a dashboard, and handle data sets and statistics.

There is a real risk of a shortage of marketing talent, combined with deep expertise in statistics and machine learning.

 

What marketing strategies can boost ecommerce sales?

To boost ecommerce sales, ensure that you have compelling and memorable product listings, descriptions and images. Ensure your website is responsive and current. Websites which are easy to navigate, mobile friendly and all work to extend your brand. It’s also important you create a sense of consistency and continuity across your platforms and social media accounts, and this can be done with a PIM tool.

Other marketing initiatives to boost sales include:

• Offering product bundles to increase order value
• Offering free shipping or free returns
• Incentivising purchases
• Use personalisation tools to suggest products for customers