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.

As data becomes increasingly abundant, harnessing its power is no longer a luxury but a necessity for staying relevant in the market. CDP plays a critical role in transforming how businesses understand, engage with, and retain their customers. In fact, according to a survey, the global market value of the Customer Data Platform is forecasted to reach $19.7 billion by 2027. 

Although CDP provides the necessary competitive edge, enterprises are skeptical when choosing the best CDP solution to incorporate into their marketing funnel. 

To help C-suite leaders make the right decision, we have shortlisted the top 8 innovations in CDPs contributing to enhanced marketing efforts. Before unveiling the golden nuggets of these innovations, let’s explore a few things businesses need to consider while choosing a CDP solution. 

Things to consider while choosing a CDP 

With so many CDP players across the globe, creating a fit-to-needs checklist for your business goals is an ongoing juggle of picking suitable consideration factors. Enlisted below are a few questions you must ask yourself before picking a CDP solution:

• How well does the CDP solution fit into your technical requirements?
• Is the solution compliant and adhering to data privacy laws? 
• Does it offer automated data export into the existing marketing tools? 
• Can the system support real-time data streaming and batch processing to keep all channels operating with updated customer data?

A Recap of The Top 8 CDP Advancements in 2023

1. Blueshift’s Free CDP Starter for Cross-channel Engagement

Blueshift’s Free CDP Starter innovation came as an optimistic development for all the data-savvy marketers. Its recent update enables users to unify their customers’ data for cross-channel engagements in a matter of minutes. 

Besides that, it features capabilities to ingest real-time events clubbed with relational data from various sources, streamlining them into unified customer profiles. For instance, it can automatically build up to 10,000 unified profiles, enabling marketers to do personalized targeting. 

2. ActionIQ’s Integration with AWS Cloud to Support Precise View of Customer Profiles

As an initiative to enable a precise and comprehensive view of customer profiles, ActionIQ integrated AWS Entity Resolution into its existing identity offering solution. This solution focussed on developing data pipelines that enabled identity resolution and enrichment. 

In return, it offers more flexibility to marketers, further modernizing their customer data stack for enhanced and tailored user engagement. 

3. Adobe’s Innovation For Precise Personalization

Precision plays an essential role when it comes to bringing personalized experiences for end customers, and thus Adobe innovated its CDP technology. Enters the Adobe Real-Time Customer Data Platform, entailing the following key features: 

• Predictive insights based on real-time customer profiles, fueling personalization across industries
• Evaluation of real-time data of 30 trillion audience segments, enabling brands to test contents and message variations, boost conversion rates, and customer retention 

4. GrowthLoop’s Collaborations for Seamless Adoption of Composable CDP

The Zero to CDP solution by GrowthLoop empowers businesses to rapidly integrate a composable CDP on their Data cloud. With a core focus on speed, flexibility, and efficiency, it’s helping organizations to seamlessly lap up the 100% potential of their customer data. 

For example, its accessible data cloud integration empowers businesses to streamline their marketing goals by giving them a consolidated view of their customers’ data streaming from various sources. 

Besides that, this integration comes with Generative AI capabilities, letting marketers derive AI-driven insights from target audiences without compromising accuracy and data integrity. Other features include self-serve marketing access, enabling marketers to effortlessly build audiences into their existing martech tools for data-driven marketing campaigns. 

Also Read: Top 15 Customer Data Platforms in the US

5. Syntasa’s Data-ready Capability For Seamless Collaboration

One of the biggest challenges that most marketers face is segmenting and reaching their target audiences with reduced time-to-value and operational costs. Here is where Syntasa’s latest data-ready innovation comes into the picture. 

This innovation reduced the time-intensive process of coordinating data requests for scaling CDP projects which was initially a castle to achieve. It enables seamless collaboration between data and marketing teams to address data quality issues, further enhancing CDP operations. 

6. Zeotap’s Non-Customer Entity Data Tool For Insights Beyond Traditional

In December, Zeotap launched its “Non-Customer Entity Data” feature, enabling businesses to integrate both customer and non-customer entity data with utmost ease. This transformative tool broadens the scope of data integration beyond the usual customer-centric-identifier approach, accelerating the effectiveness of marketing strategies. 

Unlike other CDPs that require data to be tied directly to a user identifier, Zeotap’s recent update enables the integration of both customer and non-customer entity data. Consequently, it helps marketers to have a holistic view of a unified customer profile with rich and diverse data points for personalized targeting. 

7. Capstone’s Audience-intelligent Integration with First-party Customer Data

In a world where engaging with new-in-market audiences without compromising their data is paramount for brands, Capstone introduced its audience-intelligent platform. This audience management platform enables marketers to ingest and normalize their existing customer data to craft result-driven marketing campaigns with maximum ROI.  

Compared to other data platforms offering assumption-based data, it provides marketers with more certain and deterministic records of over 14 billion customers. Utilizing these records, brands can create privacy-compliant and personalized campaigns that are essential for marketing efficacy. 

8. Twilios’ Upgraded CDP To Club Real-time Customer Profiles for B2B Marketers

Coming as a favorable update for B2B marketers, Twilios debuted Segment B2B Edition with Linked Profiles. It groups B2B customers from similar industries in real-time, enabling marketers to create effective and relevant customer engagement materials.

For instance, it can club the LinkedIn profiles of leaders working in retail or similar industries together, allowing marketers to communicate with them in an interactive and personalized way. 

Wrapping it up,

Innovative CDP tools have helped marketers for good reasons. It is deemed indispensable for businesses, shedding light on its key roles in enhancing customer experiences, optimizing marketing strategies, and ultimately driving sustainable growth in the digital era.

The post Top 8 CDP Innovations in 2023 That Grabbed Attention appeared first on MartechView.

As the jingle bells start their annual serenade, brands dust off their ‘tech’ tinsel and decking the digital halls with festive cheer. Holiday-themed campaigns have always been an important brand segment to improve customer engagement, significantly contributing to the business’s seasonal revenue.

We’ve picked a few thought-provoking ideas that brands have incorporated into their holiday marketing strategy in 2023. But before we spill the beans, let’s explore how these holiday marketing campaigns help brands and why they are the need of the hour.

Why are holiday marketing campaigns important?

A survey by Gartner predicted that 30% of consumers will shop for gifts this holiday year-round by 2026. What does it imply? A chance for brands to be more innovative while shooting their holiday marketing campaigns down the years. They can prompt target audiences to make holiday-related purchases as an effective repercussion of their festive-themed marketing offers and special discounts.

Brands leverage the outcomes of such campaigns and extract valuable insights from customer data to shape their marketing objectives. Furthermore, they harness the power of marketing automation tools and the capabilities of generative AI, machine learning, and other cutting-edge technologies to enhance and optimize their outcomes.

5 Inspirational Holiday Marketing Campaigns That You Shouldn’t Miss

1. Coca-Cola‘s Santa Heavy Holiday Campaign with AI Integration

Have you added Santa to your Christmas card this year?

Coca-Cola’s “The World Needs More Santas” campaign is just what you need to share your festive cheer with the world. The brand has expanded its generative AI-fueled “Create Real Magic” platform, featuring digital and experiential elements, letting consumers design digital holiday cards with brand assets. Besides that, it represents their iconic depictions of Santa Claus. Coca-Cola also promises to feature consumer-created artwork on digital billboards throughout the world.

This global effort is set to be adapted to over 80 markets worldwide. The motive is to create a larger marketing push through other engaging activities, such as digital quizzes, Christmas truck tours, on-pack competitions, and out-of-home elements.

For instance, QR codes on product packaging direct users to the quiz, providing them with opportunities to win prizes, including a trip to Rovaniemi, Finland, recognized as Santa Claus’s “official” hometown.

Developed with WPP Open X, led by VML and supported by Grey, Ogilvy, Essence Mediacom, Hogarth and JKR. 

2. Air Canada‘s Traditional Holiday Drive with a CGI Technology Twist

Here’s a story: Two button-cute nutcracker bears find themselves on separate sides of the tree after a year of being tucked away together. Distraught at the thought of spending the holidays apart, they make a series of heroic efforts to reconnect, including zip-lining along a string of lights and collapsing a gingerbread house.

Now, with a little help from Air Canada, the tiny wooden bears reunite with a heartwarming embrace in a dramatic and final leap of faith.

As an initiative to reunite customers with their families, Air Canada incorporated CGI technology into its video marketing strategy to deliver an exceptional user experience for customers who onboarded its flights.

Developed with FCB handling strategy and creative; animation by Hornet; audio by Cult Nation & OSO.

Also Read: StreetEasy Illustrates House-Hunting Odyssey as Renaissance Art

3. Target‘s Digital Campaign with Media Mix Optimisation Strategy

Target got its priorities right. Observing the concurrent nature of its consumers—spending more and less—the brand created a marketing strategy that catered to mindful spending customers and also converted those with tight pockets.

The brand’s “However You Holiday, Do It For Less” campaign focuses on inspiring shoppers with a message about affordability. It promotes the seasonal assortment of day-to-day items, leveraging media mix optimization strategies.

The campaign encompasses over 100 spots, with creatives running across TV, digital, video, social, digital display, paid search, and audio channels. The strategy is implemented in response to the fact that 75% of Target customers now initiate their digital shopping journeys on mobile devices, emphasizing the significance of platforms such as social media.

In addition to prioritizing dollar value, the “do it for less” message aligns with Target’s same-day services, encompassing complimentary order pick-up, drive-up options, and delivery facilitated by the retailer’s Shipt platform.

Developed with Agency Mother

4.  Pizza Hut‘s “Leave a box, get a box” with QR Code Facilities

When did you last show your gratitude to your late-night pizza delivery driver? Pizza Hut has an idea that is fresh out of the oven!

The “Leave a box, get a box” campaign serves its customers and delivery partners.

The chain doled out 50 of its doormats to its target consumers, featuring their food items throughout the holiday season. Consumers can order online by scanning the QR codes on a first-come, first-served basis.

Once the delivery driver brings the order, they can scan the QR code, which directs them to a microsite where they can redeem gift cards. Through the doormat concept with a QR code system, the chain also wants to encourage the habit of gratitude among young consumers while offering them enhanced digital experiences.

5. O Boticario’s “Make Your Love Present” Campaign with 360° communications strategy

O Boticario’s strategy was to tug at people’s heartstrings. A film set to I’ll Be There by the Jackson 5 invites people to “Make Your Love Present” and fully experience everyday life.

For its official launch, the brand took to the skies! In front of the obelisk in Ibirapuera Park, 350 drones formed a QR code in the sky.

The campaign sparked an illustrative approach with a promotional video to generate a deeper connection with the target audience. Consequently, this helped the brand to collect responses from their customers on social media and gather behavioral insights from target audiences.

Developed with AlmapBBDO and LIVE

Also Read: StreetEasy Illustrates House-Hunting Odyssey as Renaissance Art

In summary, brands have always been juggling tech-based and emotional triggers in their marketing strategies to drive target audiences and successful ROIs. The holiday season is among the best opportunities to experiment and spread love.

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62% of marketers hold the belief that generative AI will enhance human creativity, augmenting distinct human qualities like intuition, emotion, and context understanding, according to a recent report.

As the world increasingly relies on generative AI for nearly every innovation, the tech and industry anxiously awaited for the other shoe to drop. Enter The European Union AI Act.

The Act is poised to impact the utilization of specific AI tools by enterprise businesses, particularly those employed in decision-making processes across various domains such as housing, cybersecurity, workforce management, and advertising. 

What does the Act entail?

The Parliament and Council negotiators reached a provisional agreement on the EU AI Act (European Union Artificial Intelligence Act). This regulation protects the nation’s fundamental rights, democracy, and environmental sustainability from high-risk AI innovations. 

The EU AI Act has established obligations for AI models based on their potential risks and level of impact. Enlisted below are a few of them: 

Prohibitions of AI Applications

After recognizing the potential threat to citizens’ rights and democracy posed by a few AI models, the co-legislators have agreed to ban the following AI applications:

• Using biometric categorization systems that leverage sensitive characteristics, such as political, religious, philosophical beliefs, sexual orientation, race, etc.
• Scraping untargeted facial images from the internet or CCTV footage for creating facial recognition databases
• Installing emotion recognition systems in the workplace and educational institutions
• Incorporating social scoring systems based on social behavior or personal characteristics
• Leveraging AI systems that manipulate human behavior to circumvent their free will, exploiting people’s vulnerabilities (due to age, disability, social or economic situation).

Obligations for General AI Systems

General Purpose AI Models will have to adhere to the transparency requirements initially proposed by the European Parliament. For example, drawing up technical documentation that complies with EU copyright law and disseminating detailed summaries about the content used for training.

High-impact GPAIs with systemic risk will be evaluated based on certain criteria, such as reports on serious incidents, cybersecurity, and energy efficiency. 

How are the global economies and businesses reacting to the EU AI Act?

Ian Liddicoat, Chief Technology Officer and Head of Data Science at Adludio said:“The formulation of regulations in the EU has been caught short by the stellar growth in LLM models trained on vast amounts of data. Policymakers did not anticipate the number of use cases, the useability, or the sign-up rate.” 

“Tech companies will resist this if it is seen to stifle innovation. Suppose the regulations restrict or limit the application of foundational models. In that case, the EU will push the ability to manage such regulation into the hands of the US, where many of the largest tech companies interested in AI are domiciled.”

There is a need for policy and an ability to manage the inherent security risks in LLMs and AGI models. But if this is draconian and not thought through or seen to stifle innovation – which will lessen the appetite for investment in the EU, it will likely backfire, he concluded. 

When other countries like Britain, Japan, China, and the USA are all set to implement AI regulations after the newly imposed EU AI Law, the Indian government isn’t imposing any specific law to regulate AI development.

Pierre Pinna, founder and  CEO of IPFConline Digital Innovations, also expressed his thoughts in a thread, saying, “Regarding the regulation of open-source AI models, the EU AI Act legislation has finally included restrictions for foundation AI models, but has granted broad exemptions to open-source ones, which are developed using code freely available to all developers so that they can modify the native models to create their ones, and also improve the original models. It may sound like a good idea, but it just “sounds like” a good idea, it’s a false good idea!”

Bernd Greifeneder, SVP & CTO at Dynatrace, said, “In the search for greater transparency around general purpose AI, it will be important to highlight how these can be combined with other, more explainable forms of AI – such as causal and predictive. These types of AI are fundamentally different from general-purpose models as they do not follow a probabilistic approach. Instead, they are built for precision, using graph-based and statistical models that leverage domain-specific, contextualized data. This makes them better suited to specialized use cases and more resistant to hallucination and bias. They are also built to be transparent, so users can trust the answers they generate to drive reliable automation rather than that insight being hidden within the ‘black box’ of the AI. The EU AI Act will get off to a great start if it can clarify these key differences between AI models.”

In conclusion, the EU AI Act emerges as a pivotal force shaping the landscape for technology and business, provoking a delicate balance between regulatory control and fostering innovation. As industry experts voice concerns over potential stifling effects, the nuanced impact on both sectors remains to be seen, with the potential for transformative changes and challenges ahead.

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