Blog post

Blog Article

Unlocking the power of online media and omnichannel sales through machine learning

Nikhil Raj
No items found.

November 3, 2023

One of the keys to success in the retail media landscape lies in understanding how to fully unlock the power of your first-party data from online media to omnichannel sales. If one is able to identify impressions across all touchpoints, tie them back to a unique customer ID (i.e., loyalty identification), attribute sales across those impressions, and assess incrementality, then they can optimize their online media to drive more efficient sales across channels. Retailers require this on their first-party ads (search, onsite display/video, etc.) and need to partner with publishers to associate IDs directly or through 3rd parties in a privacy-compliant environment. It's a complex puzzle that, without the aid of Machine Learning (ML), can be daunting to solve.

Let's delve into this multifaceted issue and explore how ML can help us not only connect the dots but also drive incremental sales and optimize omnichannel sales.

The customer view with loyalty ID at the center

Placing a unique customer ID, often a loyalty ID, at the heart of your customer view can create an effective omnichannel marketing strategy. Every interaction, every impression, and every touchpoint can be linked back to this unique identifier. This centralization allows you to track and understand your customer’s behavior across all channels, whether they're engaging with you onsite or in-store.

By connecting onsite and in-store actions through your loyalty ID, you build a comprehensive customer profile that goes beyond demographics and purchase history. It helps you discern how customers move across various touchpoints and what influences their purchasing decisions. This is the foundation for accurate attribution and incrementality analysis.

The challenge of attribution

Attribution, or the process of assigning sales to the impressions and touchpoints that occurred before the final purchase, is a complex challenge. Customers interact with retailers through numerous channels and over time. Some categories have only days separating impressions and sales — like groceries — while others with high-ticketed items — like fashion or electronics — can take months between impressions and purchases. This makes it difficult to pinpoint which touchpoints were most instrumental in driving a sale.

ML algorithms can sift through vast amounts of data and identify patterns, giving you insights into which impressions were truly impactful. Was the sale incremental, or was it bound to happen regardless of these touchpoints? This is where machine learning comes into play. Machine learning can help you answer this question and give you a clearer picture of your marketing efforts' actual impact.

The quest for incrementality

Once you've cracked the attribution puzzle, the next challenge is to determine incrementality. In other words, you need to find out which levers drive incremental sales — those additional sales that wouldn't have occurred without specific marketing efforts.

Machine learning models can analyze historical data, conduct A/B testing, and perform predictive analytics to unveil the true impact of each marketing touchpoint. By distinguishing between sales that would have occurred naturally and those that resulted from your marketing initiatives, you gain invaluable insights into what's working and what's not.

Optimization through machine learning

With attribution and incrementality under your belt, it's time to optimize your marketing strategy — focus on doing more of what drives incremental sales and less of what doesn't. This is another challenge that can be effectively addressed through machine learning.

ML models can not only identify the high-impact touchpoints but also help predict future outcomes. It can help you allocate your marketing budget more efficiently while considering omnichannel sales and online media strategies that deliver the best results. As your machine learning algorithms continuously learn and adapt, you can fine-tune your marketing efforts in real-time.

By leveraging first-party data through machine learning, you can stay ahead of the curve in an ever-evolving marketing world. Companies that recognize the power of machine learning, like Moloco, can help solve complex challenges of omnichannel sales through media integration, attribution, incrementality, and optimization. Moloco has found that every incremental percentage of improvement in ROAS drives additional spending.

The integration of omnichannel sales in online media, coupled with machine learning-driven solutions for attribution, incrementality, and optimization, is the future of effective marketing. With the loyalty ID at the center of your customer view and the power of large-scale sophisticated ML, you can make every impression count in your omnichannel marketing strategy. It's time to embrace the complexity and use machine learning to turn it into your competitive advantage.

Nikhil Raj

VP of Business, Retail Media, Moloco

Dark blue arrow to learn more about the subject

Editor’s choice

Unlocking synergy: The case for online retailers and marketplaces in the ads businessUnlocking synergy: The case for online retailers and marketplaces in the ads business

Delve into the strategic importance of building an ads business for online retailers and marketplaces, highlighting its impact on user experience, seller empowerment, revenue growth, and market competition.

read more
White arrow to learn more about the subject
Navigating the future: 2024 commerce media trendsNavigating the future: 2024 commerce media trends

2024 will likely see the commerce media landscape evolve, defined by the use of first-party data and machine learning for ad optimization, internal handling of media efforts, and embracing Connected TV for full-funnel marketing.

read more
White arrow to learn more about the subject
 Adweek interview with Nikhil Raj on how machine learning in retail media benefits users, advertisers, and platforms Adweek interview with Nikhil Raj on how machine learning in retail media benefits users, advertisers, and platforms

Get the complete recap of AdWeek's interview with Nikhil Raj, VP of Retail Media at Moloco, on how machine learning in retail media transforms online commerce by boosting profitability, personalizing user experiences, and increasing ad effectiveness.

read more
White arrow to learn more about the subject
How to optimize sponsored ad performance for food delivery marketplacesHow to optimize sponsored ad performance for food delivery marketplaces

Optimize sponsored ad performance for food delivery by tapping into the power of vertical-specific data — like delivery times and fees — to drive transactions and empower local restaurants with a risk-free, pay-per-order advertising model.

read more
White arrow to learn more about the subject

Ingin mempelajari lebih lanjut?

White arrow to learn more about the subject

Berlangganan Buletin Moloco

arrow top