Irvinder Ray & Maneesh Kaushal dissect MarTech’s role in data-driven decision-making

In today’s digital era, there is a whole new transformation of the marketing landscape, where businesses are required to find and identify innovative, out-of-the-box ways to engage and interact with their TG. What has emerged as a strong tool for marketers is decision making which is driven by data, empowering them to gather meaningful insights and to make informed decisions for optimising their marketing strategies. By leveraging the value of data, marketers can better comprehend the behaviour of the customers, and deliver personalised campaigns, thereby tracking performance metrics for driving business growth. Data has become the driving force behind modern marketing.

With the emergence of the internet and technology, a large amount of data about the customers as well as their behaviours is currently accessible and available to us, opening up a lot of opportunities for marketers. By taking advantage of the power of data and analytics, marketers can derive meaningful insights about the preferences of the consumers, their needs, and habits. In this regard, MarTech plays a significant role. MarTech denotes a host of tools and software assisting in the fulfillment of marketing objectives and goals.

Adgully spoke to Irvinder Ray, Executive Director, Deloitte India, and Maneesh Kaushal, Global Head, Business and Analytics, OLX Autos, to explore more about how MarTech tools play an important role in the enablement of data-driven decision-making.

Irvinder Ray noted, “The scale and the pace at which the marketing, and customer data are flowing is huge. It is impossible to analyse the same without the help of MarTech tools. Many decisions especially on digital require agility and speed like in-campaign course correction, which only Martech tools can facilitate. Market dynamics are changing fast, brands can’t be reactive to competition. Hence, analysis of data, ROI of platforms, and campaigns need to be monitored on an ongoing basis for which you need Martech. Martech also helps to evaluate the quality of inventory especially if it is at lower rates. Martech tools also helpconnect multiple sources of data, enabling one view of customers, and combining multiple applications which don’t talk to each other.”

Further, she discussed the MarTech technologies and platforms that she has found to be most effective for the analysis of the impact of marketing and non-marketing activities on business outcomes and the way they have contributed to her decision-making process. She added, “We used AI-ML driven marketing ROI platform Demand Drivers, which helped us get a holistic view of marketing ROIs. It also considers macro-economic factors and competition marketing activities that are impacting the business. Since it is a DIY platform, ROI models are refreshed with new marketing data in no time. We can now have an always-on measurement engine to guide us on our budget optimisation, build simulations, and track month-on-month progress against the forecast.”

Maneesh Kaushal discussed how the adoption of data-driven decision-making has transformed marketing strategies across various industries and what it indicates about the changing dynamics of business engagement with audiences.

Data-driven decisions offer in-depth insights into customer behaviour, preferences, and demographics by leveraging the power of data analytics and technology. Kaushal listed the ways in which these insights empower marketers to target specific audiences more effectively:

Understanding Customer Behaviour: Purchase History: Analysing past purchases helps identify trends and preferences. This information allows marketers to anticipate future buying behaviour.

Website Interactions: Tracking website interactions provides insights into the customer journey, highlighting the pages they visit, time spent, and actions taken. This helps in optimising the online experience.

Segmentation and Personalisation: Demographic Segmentation: Data allows marketers to categorize customers based on demographics such as age, gender, location, income, etc. This segmentation helps tailor marketing messages to specific groups.

Behavioural Segmentation: Grouping customers based on behaviour enables personalized marketing strategies. For example, segmenting by frequent buyers, occasional buyers, or non-buyers.

Predictive Analytics: Forecasting Future Behaviour: By analysing historical data, predictive analytics can forecast future behaviour. Marketers can anticipate what products a customer might be interested in, allowing for proactive marketing strategies.

Improved Targeting:

Lookalike Audiences: Using data to identify characteristics of existing high-value customers allows marketers to find similar audiences. Platforms like social media often provide tools for targeting “lookalike” audiences.
Personalised Marketing Campaigns: Tailored Messaging: With insights into customer preferences, marketers can create personalised content and messaging. Personalisation enhances engagement and increases the likelihood of conversion.
Optimising Marketing Channels: Channel Performance Analysis: Data helps assess the effectiveness of different marketing channels. Marketers can allocate resources to channels that resonate most with their target audience.
Real-time Analytics: Adapting Quickly: Real-time data allows marketers to adapt their strategies quickly. For instance, if a campaign is not performing well, adjustments can be made promptly.

Customer Retention Strategies:

Identifying Churn Indicators: Analysing customer behaviour data can help identify indicators of potential churn. Marketers can then implement retention strategies, such as targeted offers or personalized communication.
Refining Strategies: Marketers can use A/B testing to experiment with different approaches. By analyzing the results, they can refine their strategies based on what resonates best with their audience.
Attribution Models: Data-driven decisions provide insights into the effectiveness of marketing campaigns. Marketers can measure the return on investment (ROI) for each campaign, allowing for better resource allocation.

A/B Testing:

Refining Strategies: Marketers can use A/B testing to experiment with different approaches. By analysing the results, they can refine their strategies based on what resonates best with their audience.

Measuring ROI:

Attribution Models: Data-driven decisions provide insights into the effectiveness of marketing campaigns. Marketers can measure the return on investment (ROI) for each campaign, allowing for better resource allocation.

“In summary, data-driven decisions empower marketers by providing actionable insights into customer behaviour, preferences, and demographics. This enables them to tailor marketing efforts, optimise campaigns, and ultimately increase the effectiveness of their strategies in reaching and resonating with specific target audiences,” Kaushal added.

Further, he elaborated on the ways data-driven decisions offer in-depth insights into customer behaviour, preferences, and demographics, and how those insights empower marketers to target specific audiences more effectively. He also discussed the challenges associated with traditional marketing mix models and how these challenges impact the efficient use of data-driven decision-making.

Kaushal noted that traditional marketing mix models, often associated with the 4Ps (Product, Price, Place, Promotion), face several challenges in the context of today's complex business environment. These challenges can significantly impact the efficient use of data-driven decision-making. Here are some key challenges:

Limited Scope of Metrics:

Challenge: Traditional models often rely on a limited set of metrics, such as sales volume and market share.
Impact on Data-Driven Decision-Making: In the era of big data, relying solely on basic metrics limits the depth of insights that can be gained. Data-driven decision-making thrives on diverse and granular data points for a more comprehensive analysis.

Inability to Capture Online Behaviour:

Challenge: Traditional models may struggle to capture and integrate data from online channels and digital platforms.
Impact on Data-Driven Decision-Making: As digital marketing becomes increasingly important, missing out on online behaviour data can lead to an incomplete understanding of customer interactions. Data-driven strategies benefit from holistic data integration.

Long Time Lags:

Challenge: Traditional models often involve lengthy data collection and analysis processes.
Impact on Data-Driven Decision-Making: In the fast-paced digital landscape, delayed insights can result in missed opportunities. Real-time or near-real-time data is crucial for agile decision-making, and traditional models may not provide the speed required.

Attribution Challenges:

Challenge: Traditional models may struggle with accurately attributing sales and conversions to specific marketing channels or touchpoints.
Impact on Data-Driven Decision-Making: Attribution is critical for understanding the impact of each marketing effort. Inaccurate or incomplete attribution can lead to misallocation of resources in data-driven decision-making.

Inflexibility in Adapting to Change:

Challenge: Traditional models may be rigid and slow to adapt to changes in consumer behaviour, market trends, or competitive landscapes.
Impact on Data-Driven Decision-Making: Data-driven decision-making relies on adaptability and responsiveness. Models that cannot quickly adjust to changes may result in strategies that are no longer effective.

Lack of Customer-Centricity:

Challenge: Traditional models may not prioritize a customer-centric approach, focusing more on product-centric strategies.
Impact on Data-Driven Decision-Making: Data-driven approaches thrive on understanding and responding to customer needs. Neglecting the customer perspective can hinder the effectiveness of data-driven strategies.

Overemphasis on Offline Channels:

Challenge: Traditional models may overemphasise offline channels, neglecting the growing influence of online and digital channels.
Impact on Data-Driven Decision-Making: In the digital age, online channels play a significant role in shaping customer experiences. Data-driven decision-making requires a balanced consideration of both offline and online channels.

Difficulty in Measuring Brand Perception:

Challenge: Traditional models may struggle to measure intangible factors like brand perception and customer sentiment.
Impact on Data-Driven Decision-Making: Understanding brand sentiment is crucial for effective marketing. Data-driven approaches leverage sentiment analysis and other advanced techniques to measure and respond to brand perception.

Complexity of Multichannel Marketing:

Challenge: Traditional models may find it challenging to integrate and analyze data from multiple marketing channels cohesively.
Impact on Data-Driven Decision-Making: Multichannel marketing is common today, and data-driven decision-making requires the ability to journey.

Privacy and Ethical Concerns:

Challenge: As data collection becomes more extensive, concerns about privacy and ethical data use become more pronounced.
Impact on Data-Driven Decision-Making: Marketers must navigate the delicate balance between utilizing data for insights and respecting customer privacy. Violations of trust can have severe consequences.

Kaushal noted, “The challenges associated with traditional marketing mix models can impede the efficient use of data-driven decision-making by limiting the scope of metrics, failing to capture online behaviour adequately, introducing time lags, struggling with attribution, being inflexible to change, lacking a customer-centric focus, overemphasising offline channels, difficulty in measuring brand perception, dealing with multichannel complexity, and raising privacy and ethical concerns. Embracing modern, data-driven approaches helps overcome these challenges and enables marketers to make more informed and agile decisions in the dynamic business landscape.”

On how Deloitte has played a role in reshaping the marketing approach of OLX by the rapid analysis of the impact of marketing and non-marketing activities on business outcomes, Maneesh Kaushal shared, “Here are the key pointers where Deloitte’s expertise helped us in the future looking for effective marketing investments:

Data Analysis and Insights:

Deloitte leveraged its analytics expertise to analyze data from various sources, including marketing campaigns (both digital and non-digital), user behaviour including various demographics, and business outcomes (Booking) on user transactions. This analysis may provide insights into the effectiveness of marketing strategies and identify areas for improvement, change in Marketing strategies from one channel to another, or dealing with multiple geographies with different campaigns/ Marketing Mix.

Attribution Modelling:

The in-house customised Deloitte Martech solution (MMM) was trained in a way that could suit the purpose of our problem statements/ business outcomes. It’s an advanced attribution model to better understand the impact of different marketing and non-marketing activities on KPIs. This involves determining which channels or touchpoints contribute most to conversions and business success.

Ease of use:

Any marketer can use it like plug & play, in terms of estimated investment, a probable mix of channels, and a mix of campaigns & can see expected outcomes in a real-time scenario with an accuracy of more than 80%.

Strategic Recommendations:

Based on the model, Deloitte could provide strategic recommendations for optimising OLX’s marketing mix. This might include suggestions for reallocating the budget, refining targeting strategies, or adjusting messaging to better resonate with the target audience.

Technology Integration:

Deloitte may assist OLX in integrating advanced marketing technologies and tools to enhance data collection, analysis, and reporting capabilities. This could involve implementing or optimising customer relationship management (CRM) systems, analytics platforms, or marketing automation tools.

Agile Marketing Practices:

In a rapidly evolving digital landscape, Deloitte could help OLX adopt more agile marketing practices. This involves iterative testing, learning, and adapting strategies based on real-time data insights to stay responsive to market changes.

Training and Capability Building:

Deloitte could provide training to OLX’s marketing teams, building their capabilities in data-driven decision-making. This could include workshops on analytics, interpreting data, and utilising insights to inform marketing strategies.”