Data Analytics for Personalised Advertising

There is a reason why personalised advertising is an axiom in the marketing realm. It is the silver bullet to reach the target audience, which drives conversions and ultimately, revenue. In the current era of digitalisation, data analytics has revolutionised the way businesses operate, and personalised advertising is no exception. With data flowing from multiple sources such as social media, websites, and market surveys, companies have access to vast amounts of data and by leveraging this data through the right tools and techniques, businesses can create targeted and personalised advertising campaigns that are more effective in reaching their intended audience.

What is personalised advertising?

Personalised advertising involves tailoring marketing messages and content to individual consumers based on their preferences, behaviour, and interests. This approach goes beyond generic advertisements that are targeted to a particular demographic or geographic region. With personalised advertising, businesses can deliver customised messages to specific individuals, which can increase the chances of conversion and boost customer loyalty.

Types of data analytics and how they can be used for personalised advertising

  • Behavioural analytics

Behavioural analytics involves using historical data to identify the behaviour of customers and thereby leading to more personalised targeting. For example, a business can use behavioural analytics to identify customers who are more likely to purchase a particular product or service, which helps them create targeted advertising campaigns that are more likely to convert.

Apart from the entertainment industry, there are other industries that can take advantage of behavioural analytics. Walmart dug into its transaction data and discovered prior to hurricanes, the sale of strawberry-flavoured Pop-Tarts and beers rose by seven folds. This unearthing allowed it to better manage its inventory levels for these products.

Another case in point: A famous fast food chain in Thailand leveraged the data and findings generated from ADA’s analytics tools: Audience Explorer, Location Analytics, and Consumer Profiler to anticipate its customers’ purchases. This strategy enabled it to reap substantial benefits, from a 12% increase in daily sales to a 4% rise in brand consideration, and even its engagement successfully gained momentous traction.

  • Affinity analytics

Affinity analytics involves the assessment of customer preferences, behaviour or interests and using that data to personalise advertising messages. For example, if a customer is identified to have a particular interest towards gaming, affinity analytics can be used to deliver targeted advertising messages that promote a product related to gaming.

An increasing number of companies are placing their investments in scaling their content creation and AI-powered decision-making in order to target their customers better. Apart from leveraging first-party data such as browsing behaviour on mobile apps, customer feedback, and chat transcripts, companies could also leverage third-party data to capture each customer's unique preferences and considerations in making a purchasing decision. Affinity analytics on first-party/third-party data is crucial in growing their business.

According to Statista, 37% of brands managed to leverage analytics of first-party data, which can be real-time and historical, to personalise their customer experience in 2022. Google and Boston Consulting Group also discovered brands that use first-party data for major marketing functions attained a 2.9x lift in revenue and 1.5x savings in costs.

  • Social media analytics

Social media platforms provide a wealth of data about consumer behaviour, interests, and preferences. By analysing social media data, businesses can identify trending topics and interests, which can be used to create targeted advertising campaigns. Additionally, social media analytics can be used to monitor customer feedback and sentiment, which can be used to improve marketing strategies.

Besides, retargeting can be done based on the findings obtained from social media analytics. One example is Expedia which utilised the data from Facebook Analytics for such purpose. Users who clicked on an ad on Facebook and find hotels in Whistler were retargeted with another ad with customised communication on the same social media platform. Messages such as “It’s been a while since we last talked” and “We missed you” emit a more personal vibe to users who haven’t made a purchase for a certain duration.

Key takeaways

For all campaigns and initiatives that have been launched, it is essential that proper measurements are in place to track their effectiveness. By measuring key performance indicators (KPIs) such as click-through rates, conversion rates, and customer engagement, businesses can identify which advertising campaigns are most effective and adjust as needed. As personalisation are done based on very intelligent, educated presumptions, it is best to fall back on the results and your target audience’s reaction as well as feedback on your efforts. Invest in data analytics to make the most out of your advertising efforts.

Challenges of data analytics in personalised advertising

Despite the many benefits of data analytics for personalised advertising, there are also some challenges.

  • Data privacy

Customers are increasingly concerned about how their data is being collected and used, and businesses must be transparent about their data collection and usage policies to maintain customer trust. To ensure customer data is well-protected, data privacy regulations like General Data Protection Regulation (GDPR) and Personal Data Protection Act (PDPA) are established to ensure businesses collect and process those data responsibly.

On 27 November 2022, Twitter’s data breach sent shivers down the spine as 5.4 million users’ contact numbers and email addresses were leaked to numerous hackers. Such an incident is predicted to inflict great damage to Twitter’s finances and operations as it is found to infringe on at least one of the GDPR’s provisions. Therefore, businesses must ensure that their data collection and processing policies are in line with the regulations of the regions in which they operate, or they might end up in costly lawsuits and penalties.

  • The complexity of data analytics

To effectively use data analytics for personalised advertising, businesses need to have the right tools, technologies, and expertise. They also need to be able to interpret the data correctly and make informed decisions based on the insights gained.

The introduction of big data that led to a large collection of data imposes huge pressure, especially on risk managers and related employees. As manual data processing is very time-consuming and poor data quality may entail, it is necessary for a company to sufficiently invest in the right resources, both people and infrastructure to ascertain that meaningful and actionable data is collected. This is indeed easier said than done as more data warrant additional analytical stratums for data visualisation. Plus, people who are not trained to handle a massive amount of data will likely draw the wrong conclusions which can cause a cosmic dent in a company’s survival.

  • Ad fraud

Ad fraud involves falsely inflating advertising metrics, such as clicks and impressions, to make an advertising campaign appears more effective than it is. Uber was embroiled in a lawsuit with its former agency, Dentsu’s Fetch as the former was charged for artificial app installs and clicks which consumed two-thirds of its $150 million digital advertising budget. And this incident is just one of the multiple court battles Uber has with other ad networks for similar issue.

Data analytics is a still boon to advertisers

All in all, data analytics remains to be a critical component of personalised advertising. By leveraging data analytics techniques, businesses can create targeted and bespoke advertising campaigns that are more effective in reaching their target audience. At the same time, businesses must navigate the challenges associated with data privacy, data complexity, and ad fraud. With the appropriate approach, data analytics can be a valuable tool in helping businesses form meaningful connections with their customers and achieve noteworthy commercial triumphs.

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