For years, retailers have relied on AI-powered ad campaigns to fuel online growth. But many are now unlocking the opportunity of how digital marketing can drive offline outcomes — from store visits to in-store purchases.

In 2024, in-store sales still dominated retail, accounting for over 80% of transactions in the US and a large majority in Europe, even as e-commerce grew. Yet, consumer behavior continued to be influenced by online channels. A Deloitte holiday survey revealed that 8 out of 10 retail executives expected sales growth in both in-store and online channels, highlighting the critical role of the omnichannel approach. This underscores marketers’ challenge: connecting digital touchpoints to physical foot traffic and real-world sales. 

Leading retailers across industries — from autos to jewelry to fashion — are using AI-powered marketing on Google and YouTube to:

1. Generate offline sales leads
2. Increase in-store traffic and purchases
3. Maximize ROI with online-to-offline attribution
   

Here’s how brands are doing it today.

Generating Offline Sales Leads: From Screens to Showrooms

The challenge: Customers often show intent online but drop off before visiting a store or booking an appointment. Retailers need seamless ways to capture that intent in the moment and convert it into offline engagement.

How Kia turned YouTube into a test-drive generator
Kia used YouTube on Connected TV (CTV) to engage with premium audiences in their living rooms. Through Shoppable CTV ads with QR codes, viewers could instantly scan their TV screens to explore offers or book a test drive.

Impact: Kia India’s campaign for its Seltos SUV drove a 10X increase in site visits and a 3.6X improvement in verified test-drive leads.

This model applies globally. In the US auto sector, Cox Automotive estimates that 87% of car buyers research online before entering a dealership. Retailers who bridge this intent-action gap see faster lead conversion and higher-quality foot traffic.

Increasing In-Store Traffic and Sales: Omnichannel AI Campaigns

The challenge: For big-ticket or experiential categories like jewelry, furniture, or luxury goods, shoppers may begin online, but they almost always complete their purchase in-store. Marketing needs to guide them across both environments seamlessly.

How Pandora turned online shoppers into in-store customers
In Europe and Australia, jewelry brand Pandora used a mix of:

• Shopping ads to highlight policies and product info online
• Performance Max for store goals to drive awareness of promotions at physical outlets
• Local inventory ads to show consumers which products were available nearby, with click-and-collect options

Impact: Pandora saw a 21% YoY uplift in return on ad spend in Australia, and globally in 2023, it achieved a 220% YoY increase in offline revenue attributed to these campaigns.

Takeaway for Europe & US retailers: As inflation makes holiday spending more considered, formats like local inventory ads can close the gap between online browsing and in-store buying by emphasizing convenience, immediacy, and local stock.

Maximizing ROI: Smarter Measurement of Offline Impact

The challenge: Retailers often struggle to prove how digital campaigns impact in-store results. Without visibility into attribution, optimizing budgets is guesswork.

How Tanishq unlocked ROI clarity with store sales measurement
Tanishq, a leading fine jewelry brand, found that while most sales happened in-store, nearly all journeys began online. It used Google’s Store Sales Measurement solution to measure and optimize across this funnel.

Impact: Analysis revealed 26% of in-store sales had a Google Ads touchpoint, driving confidence to reinvest in omnichannel campaigns. This boosted store footfall and cut acquisition costs by 38%.

For US and European retailers, attribution matters more than ever: Google data shows that businesses using AI-powered store goal campaigns with offline sales measurement see ROI lifts of 20–30% on average, as they can better optimize their omnichannel spend.

The Blueprint: AI-Powered Offline Growth at Scale

To generate high-value leads:

• Use YouTube CTV with interactive elements (QR codes, shoppable formats) to capture intent and bookings directly from audiences relaxing at home.

To increase foot traffic & in-store purchases:

• Run Performance Max for store goals to drive awareness of promotions and leverage local inventory ads for convenient availability messaging.

To maximize ROI & attribution:

• Implement Store Sales Measurement to close the loop between digital ads and in-store transactions.

Key Takeaway

Even in an increasingly digital retail landscape, stores remain the heart of commerce. The winning formula for 2025 and beyond will be:

• Reach shoppers online with messages that inspire action
• Seamlessly connect digital touchpoints to nearby store visits
• Measure and optimize the full journey to maximize ROI

By blending Google’s AI-powered ad innovations with omnichannel retail strategies, businesses can turn searches into store visits, clicks into checkouts, and views into visits.

“Winning retailers see digital not as separate from physical, but as the ultimate driver of in-store growth.” — Google Retail Insights, 2025

The post How Retailers Are Using AI in Marketing to Drive Store Visits and Offline Sales appeared first on MartechView.

Marketing leaders have long relied on various techniques to measure and communicate their impact, but traditional digital attribution methods often fall short. This limitation has fueled a growing interest in marketing mix modeling (MMM).

A recent Gartner survey found that nearly half of marketing leaders struggle to prove their value and gain recognition for their contributions. MMM offers a compelling solution for marketing and enterprise functions like finance and supply chain, which use these models to articulate return on investment and optimize strategies.

As the demand for deeper, more frequent insights grows, MMM continues to expand its role, providing a robust framework and a crucial tool for organizations with substantial media budgets to maximize marketing efficacy.

The rise of marketing mix models

Marketing mix models have gained traction as legacy attribution methods struggle to quantify offline and brand efforts, often overemphasizing bottom-of-funnel tactics. Regulatory changes and uncertainties around third-party tracking and ad targeting have further accelerated the shift toward MMM. Reflecting its growing importance in marketing analytics, Gartner found that 64% of senior marketing leaders have adopted MMM solutions.

Also Read: Marketing Metrics: More Than Just Vanity Metrics

There are five primary use cases for MMM solutions, each catering to specific organizational needs:

• Basic mix modeling: Ideal for organizations new to MMM, this scenario emphasizes data management, model latency and adoption enablement for marketers.
• Enterprise mix modeling: Focuses on cross-functional adoption, business scenario planning and complex analytics, making it crucial for estimating all business factors impacting ROI.
• Big-budget advertising mix models: This scenario prioritizes media optimization and complex analytics and is tailored for advertisers with substantial media budgets.
• House of brands: Designed for organizations seeking to standardize and scale their MMM approach across multiple brands, emphasizing data management and media optimization.
• Self-service mix model: This model supports organizations desiring granular control over model specifications, focusing on data scientist adoption and complex analytics.

The role of genAI in MMM

The integration of genAI into MMM solutions is increasing, improving insight generation and simplifying the identification of optimal scenarios. AI-driven insights help uncover marketing performance drivers hidden across multiple data views, enabling more informed decision-making.

If you’re evaluating and selecting a marketing mix model solution, make sure to:

• Engage stakeholders across marketing, finance, data management, supply chain and executive partners to document data and output requirements, securing enterprise-wide buy-in before contract execution.
• Begin collecting and auditing two years of daily marketing and business conversion data before committing to a vendor, as data collection can significantly impact production timelines.
• Evaluate vendors based on their capabilities, industry experience and ability to address key organizational questions.
• Assess vendors’ current functionality and their integration of emerging trends and modeling techniques into future roadmaps.

Also Read: How Are Marketing Attribution Models Helping Brands Drive ROI?

As you navigate the complexities of proving marketing value, MMMs offer a powerful solution. By quantifying the overall impact of your efforts and optimizing business outcomes, MMM enables you to make data-driven decisions and improve marketing effectiveness.

With the integration of genAI and a focus on cross-functional collaboration, MMM is set to become an essential tool in the marketing arsenal, driving strategic growth and success in 2025 and beyond.

The post Marketing Mix Modeling: A Shift to Data-Driven Insights appeared first on MartechView.

A combination of tighter marketing budgets and the expanding role of CMOs has pressured B2B marketing teams to find new ways to demonstrate the value of their marketing initiatives in a way the C-suite will appreciate.

Marketers must continually explore new ways to measure marketing performance if they want to hold onto (or even grow) their budgets. Don’t worry – we wouldn’t highlight a challenge without offering a solution. Let’s look at four performance measurement techniques that can help your team prove the worth of your marketing beyond a shadow of a doubt.

Multi-Touch Attribution

Multi-touch attribution (MTA) is a technique for measuring marketing performance along each touchpoint on your buying journey. MTA can assign fractional credit to each touchpoint your customers interact with, allowing your marketing team to understand which touchpoints most influenced the customer’s decision-making.

Using multi-touch attribution can be challenging because it requires locating and defining every touchpoint, and then ensuring you can access accurate marketing metric data from each of those touchpoints. For best results, experts recommend exhaustively mapping out your customer journey and touchpoints before you get started.

From there, your team should choose an MTA model, determining how you’ll assign fractional credit to each touchpoint you’re tracking. There are several models to choose from, including linear attribution, time-decay attribution and position-based (or “u”) attribution. Choose the model that most accurately reflects how your unique customer journey works.

Also Read: Mastering LTV: Data Analytics and Attribution

Conversion APIs

LinkedIn introduced Conversion APIs (CAPIs) in November 2023 as a way to provide greater clarity and precision to advertisers in the face of evolving privacy restrictions. This new feature allows you to connect the rest of your online and offline data to LinkedIn to see how LinkedIn ad campaigns have influenced actions on your buyer’s journey.

CAPI allows you to improve your full-funnel measurement without relying on cookie-based tracking. As you continue to use CAPI to track and measure conversion performance, you’ll gain insights into where your marketing is and isn’t driving the next action from multiple types of conversion data, allowing you to optimize messaging and targeting across each stage. CAPIs become even more useful when combined with the Insight tag, which unlocks further demographic information about site visitors.

Marketing Mix Modeling

Marketing mix modeling (MMM) uses different forms of regression analysis to analyze your historical business data and estimate the impact of different marketing tactics on business performance. 

MMM can help you understand how various aspects of your marketing campaigns – including channels and audience targeted, messaging, budget and more – affected your previous performance relative to business goals. This historical context can give you a clear view of when and where your marketing is most effective and how to optimize future campaigns. 

There are three core steps to using MMM. First, define the business objectives you want to measure your performance against and settle on KPIs. Next, collect and prepare your historical marketing and outcome data. Finally, choose a regression model to compare different aspects of your previous marketing strategies to your KPIs. This will help you identify different patterns you can use to optimize future marketing.

Quality Reach Metrics

Quality reach metrics are part of performance branding, which aims to prove the ROI of brand marketing in bottom-line terms the C-suite can understand. 

Because up to 95% of B2B buyers are out-of-market at any given time, brand marketing contributes more to long-term growth than demand generation. This is achieved via memory generation. Successful brand marketing makes your brand memorable to out-of-market customers, so that when they are in-market, they remember your brand and come looking for it.

Performance branding quantifies this effect by measuring share of mind, which is a measurement of how many people positively associate your brand with the types of products and/or services you sell. Quality reach metrics use share of mind to start measuring the quality of specific brand marketing elements in impactful terms. 

There are five quality reach metrics:

• Quality creative measures how well your audience is responding to your branded content.
• Quality reach measures how effectively your branded content is reaching current and future buyers.
• Quality attention measures how effectively your branded content is capturing and retaining the attention of your audience.
• Quality awareness measures how well your content fits the context of its environment and how effectively it is building positive associations.
• Quality lift measures how effectively your brand marketing helped increase sales during a specific period.

Also Read: How Are Marketing Attribution Models Helping Brands Drive ROI?

By measuring each of these quality reach metrics, you’ll be able to point to exactly how and why your brand marketing is making a difference. It’s a great example of how B2B marketers can think more deeply and holistically about measuring performance, looking beyond final conversions to more clearly understand the buying process and how marketing influences it.

The post Marketing Metrics: More Than Just Vanity Metrics appeared first on MartechView.

Imagine the money and time wasted on a marketing campaign that could have been more effective. There are lost opportunities to convert the potential lead into a customer. This is a terrifying scenario for brands and their marketing teams. 

A survey revealed that 81% of businesses have yet to progress toward or achieve their digital business transformation goals. Here is where marketing attribution models come into roleplay. 

They help marketers to get insights into their marketing efforts, further prompting them to restructure their strategies. However, choosing a suitable marketing attribution model and its associated tools varies depending on the goals and preferences. Let’s delve deeper and understand everything about the marketing attribution model and criteria to pick the right one.

What is marketing attribution and its KPIs?

Marketing attribution is a systematic approach for giving conversion credits to specific marketing channels and touchpoints customers encounter during the purchase journey. It enables brands to identify marketing strategies that have contributed effectively to sales or conversions. Enlisted below are a few KPIs (Key Performance Indicators) for assessing the success of marketing efforts: 

• Conversion Rate for analyzing visitors who come to a website page and take desired actions, such as form completion, e-book downloads, or making a purchase
• Customer Acquisition Cost (AQC) to monitor the money spent in onboarding a new customer 
• Return On Investment (ROI) to analyze the ratio of generated revenue from  the marketing efforts to the cost of those efforts
• Customer Lifetime Value(CLV) to determine and predict customer’s overall relation with the brand by projecting net profit 

A quick overview of various popular marketing attribution models

First-touch Attribution Model

In a first-touch attribution model, 100% of the conversion credit is assigned to the first marketing channel a customer interacts with. In simple terms,  if a customer first finds a service or product on Pinterest, then Pinterest gets all of the credit for any sale after that interaction. It doesn’t matter if the client clicks on the FB ad and then reaches the website.

For instance, this model is ideal for SMEs with only a small marketing budget and limited data science expertise.

Last-touch Attribution Model

As its name speaks louder, the last-touch attribution model gives 100% credit to a customer’s last interaction.  For example, suppose the brand markets its products on various channels, and Meta ads are the last point of interaction for the consumers, influencing them to search for the product and buy it deliberately. In that case, Meta ads get the conversion credit.

For example, LinkedIn’s algorithm uses the last-touch attribution model to analyze the conversion rate.

Linear Attribution Model

The linear attribution model gives equal credits to all the channels where conversions occur. Let’s consider a scenario where a product is promoted via newsletters, Meta ads, YouTube videos, and affiliate marketing. In this case, 25% credit will be given to each channel, regardless of whether the target customer was converted through a newsletter. The drawback of this model is the difficulty in identifying the best-performing marketing tactic.

Also Read: Mastering First-Party Data in a Privacy-focused Era

For instance, this model suits go-to-market teams looking for equal credit distribution across all touchpoints.

U-shaped Attribution Model

Also known as the position-based attribution model, the U-shaped attribution model gives 40% credits to the customer’s first and last touch point. It gives the remaining 20% credits to the other touch points between the first and last touch points.

For instance, it’s a perfect model for businesses with repeat customers who already understand their consumer’s behavior and journey.

W-shaped Attribution Model

The attribution model equally assigns 30% credits each to the first, middle, and last touch before the customer is onboarded. It specifically highlights the importance of both early and last-stage marketing efforts.

For example, This model is ideal for complex customer journeys where lead nurturing and mid-funnel interactions are essential. Nevertheless, it is less popular than other models.

Custom Attribution Model

This model, tailored to unique customer journeys, enables marketers to assign custom weight to different touch points, depending on the importance of their sales cycle. Consequently, it helps businesses develop a personalized model that aligns with their specific marketing strategies, customer behaviors, and goals.

For instance, the older version of Google Analytics, Universal Analytics, used session-based insights based on custom rules. However, Google phased it out due to the complex process involved it and introduced GA-4.

Data-Driven Attribution Model

The data-driven attribution model is every marketer’s favorite, all thanks to the real-time insights it uses to calculate the touchpoints of a customer journey. Utilizing the data-driven attribution model, marketers can keep track of the source from where the user enters their website and buys the product or service.

For example, Google Analytics 4 uses a data-driven attribution model that enables marketers to quantitatively analyze the maximum number of conversions from a channel or strategy via UTM (Universal Traverse Mercator). As a result, it helps them to focus on a strategy that works well.

How do you choose a suitable marketing attribution software to drive higher ROI?

When choosing a suitable marketing attribution solution, several consideration factors may vary from brand to brand. Thus, it becomes essential to carefully weigh the following criteria before choosing an attribution tool: 

• Integration Support: Does it easily integrate with the rest of your martech stack? 
• Affordability and budget: Are you getting what you are paying for, or are the provided solutions too expensive? 
• User Experience: Is the solution straightforward and user-friendly to support smooth onboarding? 
• Goal Alignment: How well does the solution align with your marketing goals regarding app integrations and other marketing tools?  

Also Read: The Evolution of Customer Data Platforms (CDPs): From Niche to Necessity

Summing up, Marketing attribution models can be a game changer for a marketing strategy. Thus,  it becomes essential for marketers to pick the best marketing attribution tool that contributes better ROI. Considering price, integration, user experience, and marketing goals are a few ways to choose the suitable option. 

The post How Are Marketing Attribution Models Helping Brands Drive ROI? appeared first on MartechView.

Planning early for peak season campaigns like White Friday and utilizing remarketing strategies can further enhance user acquisition and engagement. It’s not just a wise opinion but an important takeaway from a recent ecommerce report by AppsFlyer. 

“Organize user acquisition campaigns ahead of White Friday and Single’s Day to benefit from the more affordable costs during this timeframe, and use remarketing strategies to guide users to the app via paid and owned media channels,” said Sue Azari, ecommerce industry consultant, AppsFlyer. Martechvibe spoke to her about the growth of mobile commerce despite the inflation and the consequential strategic shift in the mobile industry.

The conversation was necessary due to the increased mobile usage in the last few years. Moreover, the increasing availability of diverse products and services has compelled brands to prioritize mobile commerce. This shift has enabled users to conveniently access and purchase items on the go, putting the power of shopping at their fingertips whenever needed.

In response to the rise of mobile commerce, ecommerce mobile apps provide users with an enhanced shopping experience by incorporating intuitive interfaces, personalized recommendations, and seamless navigation. As a result, the overall process of shopping becomes more streamlined and enjoyable for users. However, ad fraud remains a challenge, and effective measures are being taken to combat it. 

Here are four learnings from Azari.

The rise of fingertips engagement

The pandemic-accelerated rise of mobile, coupled with more products and services ever available, demand from shoppers has driven brands to emphasize mobile commerce, on-the-go and customer engagement at the user’s fingertips whenever the need to buy arises. 

“This enhanced usability has increased customer engagement and longer session durations. Mobile apps have also facilitated greater convenience and accessibility for customers. With features like saved payment information, one-click ordering, in-store navigation, and push notifications, apps have made it easier for customers to discover products, track orders, and receive personalized offers,” said Azari.

Fighting ad fraud

Digital advertising is less exciting for customers than it is for mobile ad marketing fraudsters. Over the years, multi-billion dollars have been invested in digital advertising, and cases of ad fraud and scams are sporadically on the rise and dynamic. Nevertheless, several platforms and tools are being developed to keep fraud at bay.

For instance, Appsflyer claims to keep app marketers ahead of every threat and their marketing budget safe through a real-time, multi-layered approach that includes cluster analysis, personalized validation rules, in-app fraud rules, and post-attribution fraud detection. 

“Fraudsters attack budgets in the ecommerce sector using the same methods as in other industries, and the larger the budgets, the more interesting they are for attacks. We observe that ecommerce players are predominantly focusing on payment fraud – and neglecting ad fraud as a threat,” added Azari.

Data Analytics & Attribution Duo

“Connecting the dots between these different touchpoints and ensuring you’ve linked the right data to your app — can be a real challenge,” said Azari.  

A shopping consumer’s journey frequently involves multiple devices, platforms, and channels, encompassing both online and offline ad exposure and various touchpoints. For example, a single purchase may begin in a physical store, followed by further research on the web, and eventually result in a purchase made through a mobile app. This illustrates how consumers navigate different stages and channels during their shopping process.

“Working with an effective attribution and measurement provider can help bridge the gap to significantly improve the user experience, attribute marketing’s contribution, and ultimately increase your LTV. By measuring performance, analyzing user behavior, employing attribution modeling, conducting A/B testing, and personalizing marketing efforts, businesses can make data-driven decisions to enhance user experiences, drive growth, and achieve their business goals in the competitive mobile app landscape.”

Finally, here’s a strategic tip.

To improve the effectiveness of your marketing campaigns, prioritize enhancing the user experience at every touchpoint, ranging from the app store to the checkout process. By doing so, you can improve conversion rates, encouraging more users to complete purchases and retention rates and ensuring that customers continue to engage with your brand over time.

“Leverage the power of data analytics and attribution to measure and analyze key metrics, such as customer acquisition costs, conversion rates, and customer lifetime value. This data-driven approach will enable you to identify areas for improvement, allocate resources effectively, and make informed decisions. Also embrace a diversified mix of marketing channels, and don’t be afraid to test new creatives or new channels,” said Azari.

The post Mastering LTV: Data Analytics and Attribution appeared first on MartechView.

As the digital world continues to evolve, technology has allowed marketers across industries to go beyond ‘cookie-cutter’ messaging and tailor their messaging to the specific needs and specialities of their target audience. 

With the help of advanced data analytics and AI-driven tools, marketers can create campaigns that are tailored to a specific demographic, behavior, ethnicity or preference that a brand is trying to reach. This level of precision allows a campaign’s messaging to resonate deeply with the target audience. 

As the success of any campaign depends on its ability to ignite engagement, it is more critical than ever to harness the power of the digital in the cyber-first age and understand the key role of healthcare cost-consumers (HCPs) and patients in a marketing campaign to make it successful, worldwide.

There is no doubt that ‘life sciences’ industry, just like the others, has seen tremendous transformative growth and innovation in recent years. Communications powered by technology has made it easier and more efficient for companies to have quality interactions with healthcare professionals, resulting in successful business results. 

In this new ‘traditional’ to ‘mainstream’ marketing landscape, the primary need of life sciences stakeholders is– driving marketing decisions. With patients becoming more aware of their role as healthcare consumers and more involved in taking, healthcare marketers are also experimenting and analysing patient datasets to create strategies and processes that capture the attention of HCPs and empower them to provide the treatment needed for a patient’s recovery.

Here are some key considerations to efficiently explore the multidimensional world of tech and enhance HCP messaging strategy.

Fostering HCP Engagement in Life Sciences

It goes without saying that HCP engagement plays a critical role in the success of any healthcare marketing campaign. As life science companies evolve their commercial models of HCP engagement, they are building access and relationships as part of a larger commercial strategy. 

With the abundance of social media handles and websites, telehealth platforms and apps, EHRs, and other digital media, HCPs are able to provide their patients with better care and treatment than ever before. The only reason this has happened is because of the digital communications that have made it possible for pharma sales representatives to have real-time meetings with HCPs. 

With the unique combination of digital creative ads, personalized messaging, and the ability to track relevant healthcare professionals, Rx drug marketers are now able to demonstrate their drug live to them just by using a digital channel. HCPs are also using digital media to extract important health-related information, as well as personal learning.

AI- Powered Personalization in Messaging

Ensuring quality medical treatment for HCPs comes with a lot of pre-requisites. With HCPs enrolled with point-of-care platforms to ensure uniformity and convenience, it is paramount for life sciences marketers to acknowledge that engaging physicians through channels and time of their preference with messaging relevant to the individual customer is a crucial parameter to make their omnichannel marketing initiatives a success. 

This precision targeting is only possible through adequate utilization of data-driven research to glean valuable insights about audience behaviour, specializations of HCPs, geographical locations that are their prime target, digital channels that are more preferred by HCPs, along with different needs that can be catered to make HCP engagement a digitally coherent apparatus. 

For instance, Virtual Reality and augmented reality technologies available today has produced immersive medical experiences for healthcare professionals. The companies here can develop virtual simulations of medical procedures, medical training modules, or AR apps that offer real-time information of the product during the detailing visits. In coming years, point-of-care marketing initiatives are expected to take up a higher chunk of Direct-To-Consumer (DTC) marketing expenses. 

Utilize Patient-Centric Approach

It’s important to remember that the primary goal of a marketing campaign is to help HCPs provide quality care to patients. Life sciences that frame their strategy from a patient’s perspective have a higher chance of achieving a recall value. 

Unlike consumers, purchases in life sciences aren’t made impulsively. They’re made after careful consideration, research and decision-making. In a world where drug sales are heavily influenced by outside factors, it’s essential to empower patients with information that can help them make better decisions about their health. Engaging key opinion leaders (IOLs) and medical professionals in the creation and dissemination of these findings increases credibility and relevance to your targeted HCP and patients.

Create Effectual Pharmaceutical Digital Ads

The core agenda of any healthcare marketing campaign is to transmit intricate scientific concepts and benefit statement to multiple stakeholders, including healthcare professionals; patients; and regulators, all while adhering to company’s compliance. Also labelled as ‘Direct-To-Consumer’, the content showcased in such ads usually includes details about a drug’s use, its potential benefits, along with how the medication works to improve a specific disease. 

By directly reaching out to HCPs through such informative and creative ads, organizations would be able to successfully attain brand recognition and assist HCP make well-thought decisions while prescribing a specific drug to a patient. The core agenda of any healthcare marketing campaign is to transmit intricate scientific concepts and benefit statement to multiple stakeholders, including healthcare professionals; patients; and regulators, all while adhering to company’s compliance.

Why India’s Life Sciences Ecosystem Needs Reorientation

In recent years, India has attained significant progress in leveraging technological advancements with over 8000 HealthTech startups in India in 2023. Knowing the population size of the country, possibilities of leveraging and obtaining insights from the vast datasets available is immeasurable. While the consumer markets of India have extensively utilized digital resources for their marketing initiatives, the life sciences industry still lags in adoption of the digital. 

A massive challenge as well as opportunity indeed lies in the multifaceted nature of data. By carefully studying details pertaining to patient medical history, prescriptions, diagnostic details, among others, Rx drug marketers can secure huge amount of healthcare costs in India. This change can help create a digitally conducive life sciences marketing ecosystem. 

To encapsulate it all, the future for life sciences marketing lies in an insight driven, responsive model that intelligently coordinates between HCPs and life sciences to offer strong linkages between brand strategy and promotional execution. 

The post Enhancing HCP Engagement Through Digital Innovation appeared first on MartechView.

Companies today need a solution that combines marketing analytics with product analytics. This will allow businesses to analyze digital advertising channels and campaigns and downstream digital product behavior.

Optimizing digital marketing efforts and achieving higher ROI with digital analytics

Digital analytics is a broad category. Traditionally, businesses have used digital analytics to track digital marketing campaigns and see how those campaigns led to website conversion. 

Conversion could be different for each type of business. For an ecommerce business, it might be a purchase. For a hospitality business, it might be a flight or hotel booking. By tracking how often customers click on digital advertising and convert, businesses can view the advertising costs associated with the same digital advertising to compute return on advertising spend (ROAS).

By combining this data, businesses can determine which digital advertising merits increased investment and which should be deprioritized.

The role of multi-touch attribution

However, it is more complex than this. Customers often leverage multiple paid and unpaid channels and campaigns before converting. As a result, businesses may need to use multi-touch attribution or other attribution techniques to assign credit to the various channels and campaigns that contribute to conversion if they want to optimize their advertising ROI.

Businesses should be able to track conversion success, attribute it to different channels and campaigns, and examine long-term conversion success. Legacy digital marketing analytics products often stop at the initial conversion event but fail to account for customer behavior over the subsequent months and years. 

For example, Google Analytics limits attribution to a 90-day conversion period. However, some campaigns take longer to convert, making that conversion period insufficient.

Also Read: AI Will Breathe New Life Into Market Research

What now?

Companies today need a solution that combines marketing analytics with product analytics so that businesses can analyze digital advertising channels and campaigns with downstream digital product behavior. An integrated solution provides a more nuanced view of digital marketing efforts and increased advertising ROI. 

For example, imagine a digital marketing campaign that attracts 50 new customers who purchase an average of $27.31 worth of products, but most customers don’t purchase again in the next twelve months. Contrast that with a different campaign that brings in 25 new customers who make incremental purchases, averaging $81.93 over the next year. 

The former campaign may look better, but the latter generates more revenue and loyal customers. Over time, the latter campaign would likely generate more ROI. Still, if the digital analytics platform cannot combine marketing and product behavior over the long term, the business may use data to make incorrect business decisions.

The product-led approach

Another trend is the adoption of product-led growth (PLG) motions. Product-led growth is a different approach to customer acquisition in which businesses rely more on their digital products to generate customers than traditional marketing campaigns. Targeting customers through paid and organic advertising has become complex and expensive as customers leverage more channels. 

Many businesses struggle to generate ROI on advertising campaigns. Instead, many leading brands have decided to spend more on their digital products in hopes that customers will hear about them through word of mouth. Investments in digital products can create thousands of unpaid ambassadors for the business. 

Digital advertising doesn’t have compounding effects, so if businesses stop spending on advertising, they will stop getting new customers. However many businesses have found that investments in digital products can have a synergistic effect in the marketplace.

Product-led growth, however, requires businesses to track all aspects of product usage to improve the product experience. Without this, product virality won’t occur. Therefore, digital analytics is a core component of a product-led growth strategy. 

The faster businesses can turn product usage data into insights and product improvements, the more likely they are to gain new customers through product-led growth. If successful, product-led growth can have the best ROI of any marketing strategy.

Understanding customers through digital marketing channels and tactics

The best way to leverage digital analytics to understand customers and identify the most effective digital marketing channels and tactics is through segmentation. Segmentation, or cohorting users, places users into discrete groups based on their behaviors. For example, a business may create a cohort of customers who have added products to the shopping cart but have not purchased them. 

These “cart abandoners” can be added to a cohort and re-marketed via additional marketing campaigns. These cohorts can be generic or very specific. For example, a retailer may have hundreds of customers who have abandoned pants in the shopping cart. This user cohort can be sent a personalized email that reminds them about the pants they left in the cart, possibly with a discount offer, and maybe push some popular cross-sell products. This is an intelligent use of digital analytics to optimize digital marketing and improve overall conversion.

Cohorting can be taken further by extrapolating data from known to unknown customers. For example, let’s imagine that digital analytics data suggests that customers who purchased the most jeans were women between 18 and 25 and from London and Paris. With this data, marketers can leverage advertising networks to target the specific product (jeans) to a particular audience (women, 18-25, London+Paris). 

This level of targeting can dramatically increase the marketing effectiveness of new customers by leveraging known behavior from existing customers in the digital analytics platform.

Moving beyond short-term conversions of digital marketing metrics

Most businesses have used the same metrics to measure digital marketing campaigns. These metrics include the following:

• Entries/Visits – How often a campaign brought customers to the website/app
• Bounce Rate – How often customers came from a campaign but took no action
• Conversion Rate – The rate at which customers complete a conversion divided by the entries/visits
• Return on Ad Spend (ROAS) – Revenue generated divided by the cost to acquire customers

Beyond these metrics, we also recommend that customers focus on the lifetime value of marketing efforts. Campaign lifetime value is similar to return on ad spend but with a much longer time horizon.

Businesses should also create North Star metrics beyond just conversion. There are actions that customers can take within digital products that aren’t monetary conversions but do indicate that customers are receiving value. These North Star metrics may be more important than short-term conversions. For example, imagine that a business like Spotify had a North Star metric of “playlist shares.” 

Spotify may have found that sharing playlists indicates customer loyalty and long-term conversion. Even though Spotify may not earn revenue when users share playlists, they may consider an advertising campaign that led to many playlist shares a success, understanding that revenue will come later.

Transforming digital marketing campaigns for business success through A/B testing

A/B testing can be a powerful way to try new content or functionality. When digital marketers use marketing campaigns to drive customers to the website or mobile application, they want to optimize the experience. A/B testing provides a way to personalize the marketing experience so every customer doesn’t see the same thing.

Imagine you manage a B2B business that sells CRM software and are running a campaign to inform people about your product’s recently added customer service features. You may purchase paid search keywords related to “customer support” and send those that click to a marketing landing page to explain your value proposition. But instead of having just one landing page, you can use A/B testing and experimentation products to try many different versions of the landing page. 

Additionally, you can leverage A/B testing products to personalize the content using other known data. Suppose the person clicking on the advertisement has been to your digital property in the past. In that case, you can leverage past digital behavior to show content or features in which they have previously expressed interest. You can also leverage account-based marketing technologies to view their industry and offer relevant case studies specific to their industry. Both of these techniques allow you to leverage A/B testing and experimentation to view how different versions convert compared to others, which leads to improved conversion and maximizes your advertising budget.

Also Read: CRM vs. CDP – Understanding the Key Differences

Challenges businesses face when it comes to digital marketing optimization

Without a doubt, the biggest challenge facing businesses around digital marketing is privacy. Many of the best aspects of digital marketing rely on the ability to collect accurate data and stitch together customer behavior across multiple devices and sessions. Knowing if a customer visiting your digital property is the same one who was there last week and last month is essential. 

However, as customers switch devices and use browsers that automatically delete cookies, knowing if the customers viewing your digital properties have been there before is becoming increasingly difficult. Consumers have spoken and told the marketplace that they prefer to be anonymous. It is likely that each year, a high percentage of customers will be anonymous when they visit digital properties.

If businesses cannot determine if customers on their digital properties are repeat customers, it decreases their ability to attribute success to digital marketing efforts. The anonymization of users can make it look like the last marketing campaign is the only one that led to success. This can skew marketing decisions toward channels and campaigns that happen last, even though other campaigns and channels are essential. The inability of businesses to accurately identify users can have dramatic ramifications when optimizing digital marketing campaigns and budgets.

Businesses can mitigate this challenge by encouraging customers to create authenticated accounts with their digital property. Once customers create an account, they consent to be tracked, and all data can be associated with their known customer profile. This also creates a direct relationship between the customer and the brand instead of relying on advertising networks to find and identify customers.

The modern approach: Incrementality

Another way for businesses to mitigate the privacy challenge is to invest in “incrementality” technologies. Incrementality is a new approach that leverages machine learning and experimentation to optimize advertising spend. 

By constantly adding and removing digital advertising, incrementality can compute which advertising combinations lead to incremental conversion without knowing who the customers are clicking on the advertisements. Incrementality represents a way to optimize marketing without infringing on customer privacy. 

Best practices to maximize digital analytics by unlocking business potential

Achieving success in digital analytics is not as straightforward as it may appear. While it may seem that all you need to do is place some code on your digital properties, the reality is that businesses need to carefully consider the questions they want to answer and map those questions to the data required to answer them. 

A digital analytics implementation that aligns strategic business questions with data and reports is crucial for success. Moreover, it is essential to ensure that digital analytics implementation is set up correctly and that the data collected is of high quality. Those accessing digital analytics data must also be trained to understand what it represents and how to use it effectively. Dashboards and reports must be created and verified, including measuring metric formulas. 

Even after all of this, humans must interpret the data correctly and draw reasonable conclusions about what the data indicates. Finally, the analyzed data must be synthesized into insights and actions to improve digital products, conversion rates, or advertising spending.

Much work must be done before businesses reap digital analytics’s benefits. Based on best practices, we recommend the following:

• Spend time identifying the specific business questions you want to answer upfront.
• Collaborate with team members on data to develop the best insights.
• Take a long-term view of data to view lifetime success versus short-term success.
• Provide data self-service so anyone within the business can obtain the required data instead of hoarding data in a small group.
• Document the changes to digital products and marketing efforts due to digital analytics data.
• Quantify the incremental revenue or cost savings that digital analytics helped produce.

The post How Well Do You Understand Digital Analytics? appeared first on MartechView.

Companies that track their CI see a 60% increase in leads by reframing their position in relation to marketing trends. In today’s fast-paced business environment, it’s more important than ever for companies to leverage CI to gain a sustainable competitive edge.

Here are eight ways Fortune 1000 enterprise companies can use CI to identify opportunities, threats, and risks.

Conduct market research

One of the best ways to stay on top of your competition is to conduct market research and gather data on your industry. This can include analysing competitors’ products and services, pricing strategies, marketing tactics, and customer feedback.

Market research can provide valuable insights into the industry, as well as the specific strategies and tactics that competitors are using to gain an edge. By utilising this information, businesses can make informed decisions and stay ahead of the competition.

Monitor social media

Social media is a powerful tool for CI as it allows you to see what people are saying about your competitors and their products. This can give you insight into their strengths and weaknesses, as well as opportunities for growth. By monitoring social media, companies can quickly identify shifts in customer sentiment and respond accordingly. For example, if a competitor’s product is getting negative feedback on social media, it may be an opportunity for your company to improve its own product or marketing strategy.

Analyse competitor’s financials

By analysing a competitor’s financials, you can gain insight into their business operations and strategies. This can help you identify potential opportunities and threats, as well as areas where you can improve your own operations. 

For example, if a competitor is investing heavily in a certain area, it may be an indication that this is a growth area for the industry and an opportunity for your own company to invest in as well.

Track competitors at industry events

Given the average enterprise attends 1-2 events per month, industry events are a great way to stay on top of the latest trends and developments in your field. You can also network with other professionals and gain valuable insights into your competition. This can include attending trade shows, conferences, and networking events. 

If a competitor is present or giving a presentation, it also offers a great opportunity to understand their positioning and give insight into their strategy. By being present at these events, companies can stay informed about the latest industry trends and gain a better understanding of their competition.

Monitor public audio/video

Audio/video data is on the rise, more is produced, and people are increasingly choosing audio/video over text consumption because the learned retention rate is much higher. If you’re not tracking the audio/video data being produced by your competitors or in your market, you are missing a massive piece of the puzzle. This includes podcasts, YouTube videos, webinars, investor conferences, industry events, and more.

Today, AI-powered technology can listen, transcribe, and summarise both audio and video files to include in your analysis. Tracking these data sources is essential to get a complete picture of what is happening in your competitive landscape.

Source: Edison Research and Triton Digital

Perform SWOT analysis

A SWOT analysis is a useful tool for identifying opportunities, threats, and risks in your industry. It involves analysing your own strengths and weaknesses, as well as those of your competitors.  

A SWOT analysis can help companies identify areas of their own business that need improvement and areas where they have a competitive advantage. This information can then be used to inform business decisions and stay ahead of the competition.

Implement a competitive intelligence product

Implementing a CI product can help you stay on top of the latest developments in your industry. The top CI products centralise all data in one place — from social media, news, review sites, websites, job postings, investor reports, conferences, and more. Due to the amount of data and level of noise, most CI teams will also work with domain-expert vendors to further contextualise the information and provide decision-ready recommendations. 

By implementing a CI product, companies can quickly and easily gather and analyse data on their competitors and the industry as a whole. This can help inform business decisions and stay ahead of the competition.

Collaborate with other experts

Reach out to other experts in the industry, such as consultants and analysts, who can provide valuable insights and perspectives on your competition. Collaborating with other experts can also help you stay on top of the latest trends and developments in your field. By working with other experts, companies can gain a more holistic understanding of the industry and stay ahead of the competition.

Companies that are tracking CI are seeing substantial growth, such as:

• A 60% increase in leads by reframing positioning in relation to market trends.
• 40% accelerated thought leadership by analysing what competitors are talking about vs what the market is showing interest in.
• 35% faster time to market from innovation tracking.

CI is an ongoing process that requires constant monitoring and analysis, but the insights and advantages it provides can give a company a significant edge over its competitors. By staying informed and making data-driven decisions, companies can adapt to changing market conditions and stay ahead of the curve.

The post 8 Ways to Use Competitive Intelligence to Win appeared first on MartechView.