How will data analytics drive MarTech in 2021, amid new challenges and market demands?

Thanks to big data analytics technologies, marketers today can avail insights on customer behavior, preferences and understand market trends too. As customers' behavior, like how they shop, compare prices, spend time, consume content, discover new products, and more; changes rapidly in the digital age, brands have to be proactive to make the most of such data. MarTech, which is a portmanteau of Marketing Technology involves analyzing these trends and helping brands adapt to them or risk not reaching their target customers. MarTech strategists rely on data analytics to monetize on consumers' expectations, plan promotions and sale campaigns and many more.

Market is indeed dynamic, and since the outbreak of the COVID-19 pandemic, new patterns have emerged like the preference for e-commerce, locally produced commodities, etc. CMOs had to turn to digital marketing approaches to attract customers and stay relevant in the competition. As per the Gartner Marketing Technology Survey 2020, MarTech will be seen as a key component for recovery, with a fair share of leaders planning to increase spending in 2021, even during a period of marketing budget contraction.

This is mainly because, companies today want to be a part of digital transformation. While data is anointed as the fuel of the 21^st century, it also lies at the core of this transformation. As per a McKinsey report, companies that take a more data-centered approach to improve their marketing return on investment by 15–20%, given the roughly US$1 trillion companies spend annually on marketing globally, which is US$150 billion to US$200 billion in additional value.

The coming year will also witness brands leveraging data analytics tools to boost their profitability while ensuring personalized, customized services. Some of these data-based trends in MarTech are:

Data Unification: As the data sources proliferate, there will be demand for data unification and consolidation into a single view. This can help marketers understand the big picture and make sense of the data insights.

Moving to Big Ops: Big Ops is about managing the growing volume and variety of apps, automations, processes, and workflows operating in brands and agencies on top of that universe of data. Marketers must become Big Ops literate, since it will be implementing checks and balances to guard against data discrimination and enforcing good data ethics policies. It will also allow businesses to extract value from additional sources of data that originate beyond the firm's walls.

Location-Based Personalization: While COVID-19 pandemic saw a sudden peak in interest among customers to shop local, marketers may have to map their consumer base' needs based on their location, season and other local factors. E.g., suppose a person visits a nearby library close to a famous ice-cream parlor. In that case, the ice-cream company can use artificial intelligence, data analytics and GPS to provide discounts to lure the person to the parlor and maybe turn him into a potential customer.

Dependence on Data Visualization Tools: Tools like Tableau and Power BI will find new users, as they help find insights within an organization's data. They can connect disparate data sets, transform and clean the data into a data model and create charts or graphs to provide visuals of the data. These tools will provide actionable insights in a format that is understandable by even the least tech-savvy team member.

Identifying Invalid Traffic and Reduced Spending: As business brands become newly aware of digital advertising, they tend to spend their money on campaigns to keep business coming in. However, most of them are unaware that up to 30% of their expenses are wasted on invalid traffic. Using machine learning-based data analysis, companies can discover genuine channels that can bring better ROI, from ad investments. This also implies that some brands may opt for reduced spending and instead focus on reaching homebound customers and maintain a relationship with them.

Data Process Mining to go Mainstream: Leveraging data process mining tools will not only boost the success of a marketing strategy, sales and revenue but also minimize sales cycle time, increase conversion rates, on-time deliveries, detect their high-value customers. It will also help figure out the reason behind order changes and returned goods.

Measuring campaign attribution will be key: As marketers will use data analytics to optimize campaign planning and execution, it will also be helpful to gauge the outcomes and impact of the campaign. While campaign attribution determines the influence of a prospect's desired outcome, it also offers insights into additional resources or budget that can be needed to achieve the expected result.

Rise of No-Code Tools: As per a survey by Deloitte, 75% of organizations still have a long way to go in terms of digital maturity. One of the main reasons behind this is the lack of technical experts with proficient coding knowledge. As the name suggests, no-code (and low MarTech tools) enable non-technical marketers to generate interactive content, develop apps, and analyze data sets between cloud sources independently. And in the coming months, the need to employ no-code tools will rise.

Emphasis on visibility: Through data tracking tools in analytics software, marketers can now track customers along the journey from initial interest to final purchase, access insights driven by website cookies and click-through rate (CTR), and others. This will enable them to understand better what's working and what isn't, allowing them to prioritize expenditure in the right channels.

CDPs to replace Research firms: Customer data platforms primarily collect first-party data and are used in almost every aspect of personalized, 1:1 marketing. Basically, these CDPs are promising marketers the ability to answer important business questions without conducting research, as well as integrate and commercialize a range of digital data streams. So, if marketers need integration of customer data, or focus on analytics or complex journeys, then MarTech professionals should choose CDP.