1. How valuable is it to have curated influencer lists that include audience demographics, engagement metrics, and content samples?

Having a curated influencer lists that include audience demographics, engagement metrics, and content samples is extremely valuable in influencer marketing. It helps with precise targeting and to avoid wasted budget on influencers whose audience doesn’t match your buyer persona. Having a curated influencer list also helps with the campaign efficiency and speed as it reduces time to identify suitable influencers and streamlines outreach.

2. What are your biggest challenges when it comes to finding the right influencer partners?

The main challenge is making sure the influencer truly fits the brand not just in terms of audience size, but in how they communicate and connect with their followers. It takes time to find someone who feels like a natural match, especially when working in a very specific niche. It’s also important to find a good balance between giving influencers creative freedom and making sure the brand message is clear. Doing this well at scale can take some effort, but it’s what makes a campaign feel real and impactful.

3. To what extent does your brand prioritize influencer alignment with brand voice, values, and aesthetics?

Alignment with brand voice, values, and aesthetics is mission-critical in influencer marketing. While metrics and reach matter, alignment is what makes a partnership believable especially to the audience. Even top brands treat this as a non-negotiable filter. That's just how important influencer alignment is to every campaign.

4. What challenges have you experienced executing influencer campaigns across different regions or cultures?

One challenge would be the time zone difference. Some influencers may see your message too early or too late depending on where they're located. For platforms, that usually just means follow-ups are necessary to keep communication going. But when it comes to email outreach, we schedule messages based on the influencer’s country to ensure they receive them at the right time. This helps improve timing, boosts response rates, and keeps the campaign running smoothly.

5. How important is cultural alignment and local audience resonance when selecting influencers for global campaigns?

Cultural alignment is a key factor in global influencer success in a sense that choosing influencers who genuinely reflect the culture of their audience ensures the message is received with impact and credibility. No matter how big or popular an influencer is, without the proper audience, there's just no magic.

6. In a saturated creator economy, how critical is it to have access to fresh, vetted influencer talent pools on an ongoing basis?

Regular access to new, pre-vetted talent helps to ensure that brands can stay ahead of trends, avoid fatigue with overused influencers, and maintain authenticity by continually reaching the right audiences. Simply relying on a static list limits reach, creativity, and relevance; and the markets have a tendency to immediately notice that.

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1. How should marketing leaders balance innovation with ethical considerations to maintain consumer trust?

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1. How do you anticipate that economic pressures will influence your organization's strategy for evaluating and adopting performance marketing technologies?

In today’s environment, budget scrutiny is the rule, not the exception. Economic pressures have and continue to force us to look past shiny nice-to-have technologies and zero in on tech that delivers measurable results.

At AUDIENCEX, we’re evaluating third-party tools and simultaneously investing in building our own technology to fuel AXi, our integrated performance platform. This includes innovative predictive audience-building, customized algorithmic campaign decisioning, agentic AI tools, and analytics all working together to drive down CPAs and improve efficiency. Every product we develop - and every process we adopt - is measured against its ability to generate transparent, data-backed results and flexible scalability so we can adapt quickly on behalf of our clients to changing market realities.

If a tool can’t justify itself with transparent, data-backed results or direct time savings, it’s not moving forward, or we’re reinventing it ourselves. We’re also pushing for flexible, scalable solutions so we can dial investment up or down as market realities shift. 

2. What is the importance of having full control and transparency over omnichannel advertising, and how does AI optimization within such a platform align with your marketing objectives?

Full control and transparency across channels is imperative; without it, you create blind spots. Our objective is to maximize every dollar, so having line-of-sight into performance at the most granular level is non-negotiable. Our AXi Optimizer tool supercharges this approach. When we layer in AI, we’re able to not just react, but proactively allocate budget, test creative, and target audience segments in real time. That agility is the difference between hitting goals and missing them especially in a market that doesn’t sit still for long.

3. How would a guaranteed CPA model, which transfers risk from the brand to the agency, impact your organization's financial planning, budget allocation in scaling digital marketing efforts?  

A guaranteed CPA model definitely shifts the risk from the brand or agency partner to AUDIENCEX, but it’s not a shot in the dark. With our recently launched PriceFix - which sets a new standard for performance marketers - we leverage custom modeling based on historical and real-time data to set CPA guarantees grounded in reality. When done right, it’s actually a smarter, data-backed way to take on risk. For us, this lowers the perceived risk on both sides: the brand gets cost certainty, and we get to put our data confidence into action. That said, we also recognize that not all campaigns will fit a guaranteed model, so we’d approach this as one tool in our broader allocation strategy, ensuring it complements rather than limits our overall flexibility.

4. How do you envision intelligent automation reshaping the responsibilities of your marketing teams, and what challenges does this present for talent development?

Intelligent automation is an area that we are prioritizing as a company, with a major product launch scheduled for Cannes Lions around our new AXi Simulator product. Powered by synthetic personas modeled on real-world psychographic, behavioral, and cultural data, Simulator enables unlimited, risk-free testing across messaging, creative, and targeting variables before campaigns ever launch. By simulating real-world market dynamics with unmatched predictive accuracy and compressing validation cycles from weeks to just days, it allows us to uncover edge case risks, optimize cultural alignment, and fine-tune campaigns at scale.

While Simulator focuses on scenario planning, our other tools, such as Optimizer for in-flight adjustments, and Explorer for real-time analytics, ensure that our teams spend less time on manual tasks and more on strategy, creative thinking, and data interpretation. This shift requires us to hire and develop talent who can both leverage advanced tech and see the big picture. We’re investing in upskilling and encouraging hands-on experimentation so that our team evolves alongside our technology, not behind it.

5. How important is predictive insight into audience behavior before campaign investment, especially when aiming to identify potential cultural mismatches or message fatigue? 

Predictive insights are critical, especially now given the previously mentioned economic pressures. Before spending a dollar, brands want confidence that their message will resonate, not backfire or land flat. Once again AXi Simulator gives us that competitive edge: spotting potential cultural misses, audience saturation, or even timing issues before they cost us. It’s about shifting from reactive course-correction to proactive planning, saving budget and reputation in the process, and ultimately delivering significant performance gains for our brand and agency clients.

6. How does your organization plan to invest in and integrate AI and predictive technologies to ensure that marketing decision-making is data-informed and minimizes reliance on traditional, less agile processes? 

I recently heard a great perspective on this in The AI Breakdown podcast: companies shouldn’t build their whole strategy around AI just for the sake of it. Instead, the smarter move is to develop an AI strategy that actually supports and enhances what you’re already good at.

That’s exactly how we approach it. We start with a real business challenge or marketing objective, and then determine if AI or predictive analytics is the right tool to address it. Our investment in the AXi platform including custom algorithms, predictive modeling, and tools like Simulator and PriceFix is focused on giving teams real-time, actionable data and automating manual processes. Looking ahead, we plan to aggressively scale our investment in this critical growth pillar, accelerating our evolution from a services-led model to a fully integrated, technology-first performance platform. By embedding predictive simulation, autonomous optimization, and real-time intelligence deeper into every layer of our offering, we enable our clients to unlock durable competitive advantages and maintain precision marketing control in an increasingly volatile and algorithm-driven marketplace.

We’ll continue to integrate these technologies directly into our workflows so decisions are always data-informed and agile. Ultimately, it’s about moving away from legacy processes and making every decision faster, smarter, and more accountable. But we’re also realistic: it’s not just about installing new tools, it’s about building data readiness and making sure our people are trained and comfortable with these new capabilities. Like the podcast said, digital maturity with AI is a strategic journey, not a quick fix. For us, it’s about making smarter, more informed decisions, not just doing AI for the headline. 

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1. How are brands currently segmenting their marketing and engagement strategies to address differences in content consumption, commerce behavior, and support expectations? 

Link to the report Consumer Brand Social Engagement - https://emplifi.io/resources/consumer-brand-social-engagement-2025-survey/

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1. How is your organization leveraging AI to streamline online shopping experiences and reduce friction in the customer journey? 

Think of us as removing two different bottlenecks—one customer‑facing, one technical.

1. On‑site experience (the customer bottleneck):

• • Our Adaptive Transformer Search understands the intent behind a query, even if it’s vague, for example “red shoes for muddy trails” returns the exact SKU instead of a dead‑end results page.
  • A multimodal recommendation engine curates a storefront that’s unique to each shopper, surfacing the product you’re statistically most likely to buy next, boosting average basket value 30‑plus percent for some clients. 
  • Finally, a real‑time multi-objective bidding agent decides which sponsored product or organic product should fill each slot, so retailers monetise and grow margin without sacrificing relevance and conversion. The result is ~20 percent higher conversion rates and ~180 percent higher click‑through on sponsored slots vs. legacy keyword targeting approaches.

2. Retail‑MCP (the technical bottleneck):

• • Today, most AI assistants simulate clicks or scrape HTML. MCP lets any agent call the retailer’s stock, price, loyalty, incentives and media endpoints directly, securely and in milliseconds. 
  • OAuth‑style scope gives retailers control over which data an agent can read or write, solving 'bot blocking' and 'AI discoverability' in one move.
  • Because the protocol is open, the same purchase flow works for ChatGPT, an in‑car voice assistant, or a smart‑TV overlay. No new integrations each time a channel emerges or gets traction.

Put together, a shopper can say: “I need men’s trail runners under £120, size 11, by Friday” - the agent makes a single MCP request, finds three in‑stock options, applies a brand‑funded discount, and checks out in seconds. It collapses the entire funnel (awareness to purchase) into one conversation while generating incremental retail‑media revenue along the way.

For retailers, that means higher basket sizes, margin and media yield with virtually zero manual merchandising. For brands, it means their products surface when a high‑intent customer is ready to buy. And because the data never leaves the retailer’s environment, everyone stays privacy‑compliant and future‑proof as agents penetrate more of the commerce journey.

Particular Audience’s AI engine removes choice-paralysis for shoppers, while Retail-MCP removes integration-paralysis for every new AI interface. Together they turn ‘AI for eCommerce’ from buzzwords into a five‑second path to purchase.

2. What measures are being implemented to minimize the number of clicks and page loads required for customers to complete online transactions? 

Websites are sort of like human 'read and write' interfaces for humans. Agents promise to mitigate the need to navigate.

In a legacy journey, a shopper might type “trail shoes”, scroll 15 results, use filters, bounce, and start again (or not convert at all).

In a PA + MCP Journey, a shopper might say “I need men’s trail runners under 120 quid, size 11, Friday latest to Shoreditch”, the agent then calls Search, Reviews, Recommendations, Inventory, Payment & Shipping APIs via MCP finding 3 in-stock SKUs with relevant reviews, viable alternatives, accounting for brand-funded discounts, then take payment and organise shipping. If the customer is happy to permit it.

Whilst trail shoes make for a fine example, the early adoptions phase is mostly replenishment products with commodity products like electronics set to follow.

Ultimately this saves around ~15 clicks and 6-8 page loads per considered item.

3. What protocols are in place to ensure data privacy and security when AI agents access and interact with retail systems?

For PA's Adaptive Transformer Search and multi-modal recommendation engine, privacy has always been foundational. We built PA in a way that is designed to collect zero personally identifiable information and does not depend on any third-party tracking. We leverage internet-scale language data, real-time context and machine learning to improve relevance while preserving privacy.

While MCP provides a foundational shift towards a standardized, secure interface, it is important to note that it still inherits risks from the underlying LLM and needs constant refinement and broader industry cooperation to set and raise standards as applications proliferate. I can say that MCP directly addresses the privacy and security limitations inherent in traditional browser-based AI agents. Browser-based agents, which mimic human web interactions, struggle with security risks due to broad browser access, making fine-grained control difficult and risking exposure when injecting internal data. Shoppers are understandably hesitant to share sensitive information like credit card details with such agents. MCP is presented as a structurally superior alternative for AI agents interacting with retail systems, offering a more robust interface.

We follow a simple rule: right data, right purpose, right connection. The SaaS tools behind the APIs that an MCP considers 'tools' generally provide fantastic governance out of the box. That's what makes MCP such a compelling option to interact with AI agents.

4. What strategies are in place to integrate AI agents into the retail systems and data to enhance transaction efficiency and security? 

Instead of custom integrating a bunch of bespoke APIs, a retailer can expose a single doorway (a Retail-MCP endpoint). Think of MCP like a universal USB-C port for retail data: the agent can ask for stock, prices, loyalty points and even coupons through a quick call. 

Strategically speaking, it isn't too different to integrating a modular and composable set of services in eCommerce already. The main challenge is getting an LLM to reason in a way that makes best use of the tools we're giving it.

5. How is your organization preparing to adapt to the increase in AI-based traffic and its implications for retail operations? 

Imagine reading about SEO in 1995 so you could do something about it, and be early. If retailers knew what they know about Amazon today, how might they have taken eCommerce a little more seriously in the mid-90s?

Model Context Protocol (MCP) is a fundamental shift away from traditional browser-based AI agents that mimic human web navigation, which is inefficient, slow, costly, and faces significant security risks and indeterminacy. Retail-MCP, Particular Audience’s implementation, specifically focuses on enabling a multi-tool MCP architecture for retailers for actually awesome customer experience step ups. We're adopting manifests like .well-known/mcp/manifest.json which allows retailers to communicate accessible resources and available data to AI agents. 

As we open up to external applications, we will be encouraging retailers to selectively whitelist beneficial AI agents instead of using blanket blocking mechanisms. We're encouraging our customers to embrace MCP, starting with high-impact use cases, implementing phased rollouts, focusing on data readiness, building governance and security guardrails (like monitoring and logging all agent actions via MCP),

I think the single most exciting thing Retail-MCP is working on is the concept of 'Multi-Tool Agent Architectures', we are focused on enabling a multi-tool architecture where an agent has access to numerous tools simultaneously and can chain them to complete complex tasks. All by leveraging existing retail infrastructure like order databases, policy knowledge bases, search and CRM tools.

Particular Audience is addressing the rise of AI traffic by providing foundational technologies (ATS for semantic search relevance, MCP for efficient agent interaction) and advocating for adoption.

6. How is your organization addressing the demand for faster and efficient online shopping experiences through AI integration?

What Particular Audience contributes:

• Instant, relevant results
  • Our Adaptive Transformer Search understands the meaning of a query (“red shoes for muddy trails”) rather than filtering for exact words. Shoppers land on the right SKU quickly, no filter gymnastics or endless scrolling.
• Personalised storefronts
  • Real-time recommenders mix personalisation, similarity, cross-selling, intent capture, margin and promotions to create a different and relevant website for every visitor. Good personalisation is good prediction. 
  • Sponsored products are slotted in automatically, so retailers can monetise without jeopardizing customer experience.
• Dynamic pricing and competitor price beat automation
• • Saving customers from price comparing across the web. Giving them the confidence and/or incentive to convert.

What Retail-MCP adds on top:

1.One front door

• • Instead of stringing together separate calls for stock, price, shipping and payment, an AI agent achieves complex sequences via one MCP interaction. The retailer replies with a ready-to-pay basket, including any loyalty points, coupons or ads within a few hundred milliseconds. 

2.Built-in security and control

• • Each agent receives a time-boxed, purpose-specific key: “Read stock, place order, nothing else.” If behaviour looks odd, the retailer can revoke that key instantly.

3.Channel-agnostic speed

• • Because MCP is open, the same five-second checkout will work in ChatGPT, a voice assistant or a smart fridge, no new integration required for every time a new channel pops up. This interoperability given the rate of change we see each and every week is a critical investment.

It is still so early, many applications are at the education, inception and pilot stages. Everyone is learning, and gradually forging best practices as an industry. No profit seeking retailer wants to be late, so it's a super inspiring time to be at Particular Audience.

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1. How important is improving unaided brand awareness to your marketing objectives in the next 12 months?  

I lead marketing for a tech company, and as a B2B marketer I think unaided brand awareness is everything!  Tech needs to be top of mind in order to be considered.  When someone is considering using a technology, or for that matter any product or service, your awareness is key.  You either need to be thought of immediately, because you are clearly associated with that category, or when they do their research and see you, they immediately think of you in a different, hopefully better, way.  That second example of more aided awareness, but unaided awareness can keep you in front of the pack and force everyone else to position against you.  The biggest, best brands and ones that stay top of mind and score highly on unaided awareness.  For the next 12 months specifically, this might be the second most valuable metric I have as a marketer, second only to actual verifiable, qualified leads that convert.

2. To what extent do you believe in-content, non-disruptive advertising can outperform traditional formats in building brand equity? 

I believe it could be the way brands build trust and break through the clutter.  Interruptive ads are good for frequency and bringing a brand back to mind, but the in-content and non-disruptive model is better because you know it is not skipped, and it benefits from being in the content people know and love.  That’s why influencers do so well.  The messages are not skipped, and there is an implied trust in the creator that carries over to the brand.  That trust comes from an authentic implied endorsement.  Brands need that trust with the consumer, and if they have to borrow that trust from the context or the creator, then so be it.  If you take a portion of your budget and allocate it here, you get more awareness for your buck because it can’t be skipped, and that awareness is associated positively with a message.  That means your entire budget works smarter AND harder for your objectives.

3. Do you see embedded brand content within creator videos as a scalable and sustainable strategy for your brand? Why or why not? 

Yes.  Fundamentally, this is what we do.  We enable scalable solutions to execute this model.  For us, the audience that is attached to a show or a clip from a show, has a higher propensity to watch and engage with that content when it is shown to them, and that creates a higher attention opportunity for the brand.  When the audience is paying attention, your brand message works smarter.  I will never say this is the only way you should operate, but it definitely could be 50% of a marketers budget going forward.

4. As consumer attention shifts toward short-form, creator-driven, AI-augmented content, how is your organization adapting its messaging strategy? 

Our messaging has to be crisp and simple.  It has to be short and succinct.  It also benefits from being embedded in B2B content because that audience is ready for that kind of message.  For me, it's all about storytelling in the shortest way possible.  A good analogy is children’s books.   Those books are able to tell a story and get a point across very quickly.  When I used to read them to my kids, there was always a key message conveyed in a short time.  I think our marketing and messaging strategy has to be the same way.

5. Which KPIs are most important when evaluating a new advertising technology like RAI?

Any new technology should be able to be measured by the same KPI’s as the methods and formats that it is competing against.  For us, we evaluate our results on Unaided Awareness, Click-Through Rate, Video Completion Rate and Sales Lift.  These are standard KPIs and ones that any marketer should be focused on.

6. How important is being an early adopter of new advertising technologies in maintaining competitive advantage for your brand?

I think this is extremely important.  You always need to be thinking ahead.  Testing new technologies early allows you to unlock the value for your brand quickly, and maintain that competitive edge.  If you wait until someone else has figured it out, then you’re always going to be playing catch-up!

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1. How are digital marketing strategies evolving to meet the unique challenges of industries like law, insurance, and healthcare?

Industries like law, insurance, and healthcare operate in high-stakes environments where trust, compliance, and expertise are non-negotiable. That’s why our approach to digital marketing for law firms, insurance digital marketing, and healthcare clients at 5WPR is rooted in precision and authority. We’re leveraging data-driven strategies, like advanced audience segmentation, contextual content development, and reputation management, to ensure our clients cut through the noise without compromising regulatory integrity. It’s not just about visibility anymore; it’s about credible, high-impact engagement, at the moment that matters, that earns trust and drives business outcomes.

2. How does a unified approach to PR and digital marketing contribute to achieving organizational goals across different sectors?

When PR and digital marketing operate in sync, the result is not just amplified messaging, it’s measurable momentum. At 5WPR, we build integrated strategies that connect brand storytelling with conversion-focused tactics. Whether it’s building authority in legal services, driving lead gen in insurance, or scaling reach in health and wellness, our digital marketing agency teams work hand-in-hand with PR to align visibility with value. This alignment ensures that every media mention, social interaction, or paid campaign feeds into a cohesive narrative that supports long-term brand equity and immediate performance metrics.

3. How is the demand for personalized content shaping digital marketing approaches in sectors such as health & wellness and insurance?

Personalization has moved from being a differentiator to a baseline expectation, especially in nuanced spaces like health and wellness digital marketing, and insurance. Consumers want to feel seen, understood, and valued. To meet that demand, we’re building dynamic content ecosystems powered by behavioral data, lifecycle insights, and predictive modeling. Whether it’s a personalized wellness journey or a tailored insurance solution, we help brands show up with relevance, delivering the right message, in the right voice, at the right moment.

4. What challenges do organizations face in ensuring that digital marketing efforts comply with industry-specific regulations and ethical standards?

Regulatory complexity is one of the biggest hurdles in industries like healthcare, law, and insurance. From HIPAA and FINRA to evolving data privacy laws, the stakes are high. Our teams are proactive in navigating these challenges, integrating compliance checkpoints directly into our digital workflow. We tailor our strategies to reflect not just legal requirements, but also ethical standards, ensuring our clients maintain credibility and avoid risk while still delivering high-performing campaigns. It’s a delicate balance, but one we’ve built deep expertise around.

5. How can companies balance innovation in digital marketing with the need for transparency and consumer trust?

Innovation shouldn’t come at the cost of trust; it should enhance it. At 5WPR, we believe that the most effective digital strategies are those that blend creativity with accountability. That means deploying emerging tech like AI, automation, or immersive content, but doing so with clear value exchanges, ethical use of data, and open communication. Especially in industries where decisions impact health or financial security, brands have a responsibility to be both forward-thinking and forthright. Our job is to help them strike that balance through smart, responsible digital storytelling.

6. What trends are anticipated to impact digital marketing strategies in sectors like law, insurance, and B2B services over the next few years?

We’re seeing several transformative trends take shape: AI-powered legal search, decentralized insurance platforms, voice-activated claims processing, and more personalized B2B buying journeys. These shifts will require brands to be not only agile but also deeply attuned to client pain points. We’re helping our clients prepare by investing in long-form content, zero-click SEO strategies, and hyper-targeted paid media campaigns. Ultimately, future success will come from marrying technical innovation with human-centric design, something we’ve embedded deeply into our approach to digital marketing for B2B, insurance, and legal industries.

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1. How important is achieving a unified customer profile, and what best practices ensure its accuracy and utility? 

A unified customer profile is a detailed, nuanced, and contextual understanding a customer, a business, a household or another entity that organizations use to provide a differentiated customer experience (CX) – or to power AI, analytics, and operations. It’s a critical part of a solid data foundation that turns a company’s customer data into business value.

A unified profile must be complete, accurate, and timely for marketers and business users to completely trust that it represents the customer they’re engaging with. A few best practices elevate a true unified customer profile over simply aggregating customer data from various sources, applying a simple match and calling it a day. 

The most important is to continuously apply data quality steps at data ingestion, not downstream. Accuracy depends on not only ingesting data from all possible sources, but also in applying normalization, standardization, data enrichment and advanced identity resolution as data enters the system. This prevents bad data from entering critical downstream systems and creates a strong data foundation that enables marketers to make more confident decisions and successfully execute campaigns.

Another key step to ensure the utility of a unified profile is to make it available and accessible across the enterprise – ensuring that all users have an identical understanding of a customer. For dynamic segmentation and real-time decisioning, a common understanding results in a consistent CX across every channel – as if the brand is speaking to the customer with one voice, regardless of the interaction touchpoint.

2. What are the key challenges organizations face in unifying customer data, and how can they overcome them?

Most companies have deep organizational silos that are difficult to overcome. They’re set up operationally with the mindset that each department needs data for its own purposes. Each department has a different idea for what constitutes business-ready data, leading to a lack of standardization. As a result, marketing teams and business users never develop a complete understanding of their customers – and can’t pull off an omnichannel CX. 

From a technology standpoint, the challenge in unifying customer data is that most customer data technology fails to prioritize data readiness, which includes making sure that data is complete, accurate and timely as soon as data enters the system. A basic match of customer data whenever there is a changing key, matching that is not tuned to the desired use case (overmatch, undermatch), or even failing to correct data inconsistencies are common when customer data technology approaches data quality as anything but a core capability. 

Technology that instead prioritizes data readiness in the building of a unified profile solves for the downstream problems associated with a lack of a single customer view, and it also is instrumental in helping organizations change their mindset for how customer data is used across the enterprise.

3. How can businesses balance the need for immediate insights with the challenges of data integration and system performance? 

Data integration and system performance challenges can often make it difficult to generate immediate insights needed to provide a great CX.  This problem gets at the heart of why data readiness is so important, and why so much customer data technology falls short of extracting value from customer data when it relies on third-party solutions for preparing data for business or CX use.

Because customer data integration has a direct bearing on delivering a real-time CX, data must not only be ingested in real time, but the various sources of customer data integrations mush also be updated in real time. If various business users accept different requirements for when data must be made ready for business use, then the result – just like having different standards for data quality – will be a poor CX due to a lack of a real-time customer understanding. Data readiness assumes that immediate insights are an indispensable part of a relevant, omnichannel CX.

4. What considerations should businesses keep in mind when adopting a composable approach to their data infrastructure? 

One consideration when assembling a composable martech stack is to understand if and when data quality processes occur. Many composable CDP vendors leave data quality to someone else, thus avoiding the consequences of having different components treating data quality with different approaches. But when a composable framework includes central ownership for making data ready for business use, marketers can trust the data they’re using to build segments and execute personalized campaigns – all without having to wonder if IT is returning the latest customer record.

Because a composability framework gives marketers direct access to a unified customer profile, they can independently build audience segments and launch personalized campaigns without having to rely on IT support, allowing them to more easily focus on strategy, executing and improving CX.

Ultimately, a composable infrastructure should ultimately make it easier – not harder – to power a differentiated CX, and that is only possible when data quality is a priority.

5. What metrics should organizations track to measure the success of their CDP implementations? 

Retention, loyalty and customer lifetime value (CLV) are three key metrics for measuring the success of a CDP implementation. A robust, enterprise-grade CDP should produce significant improvements in all three, the result of transforming raw customer data into actionable insights through having a deep customer understanding. In a McKinsey survey on CX, 76% of consumers said that receiving personalized communications is a key factor in prompting consideration of a brand, and 78% said that such personalization makes them more likely to repurchase. 

Higher retention, a more loyal customer base, and an increase in CLV are the direct result of implementing a CDP that gets data right – ensuring it is complete, accurate and timely – and makes it actionable for any business or CX use case. That means a unified profile is accessible for segmentation and real-time decisioning, that it is tunable depending on the desired use case, and that it is privacy compliant.

6. Looking ahead, what emerging trends do you believe will shape the future of customer data management and personalization?

Agentic AI will play an enormous role in the evolution of customer data management and CX. There is an expectation that brands will rely on agentic AI to manage and execute an end-to-end customer journey, essentially taking the familiar chatbot experience to another level with agents representing a virtual concierge for an individual customer. 

An expectation for personalization will harden into an expectation for agents to be responsible for a consistently relevant CX across channels. Because a chatbot can now easily handle questions about a company’s return policy, for example, customers will soon expect agentic AI to be able to execute a specific return – print a label, schedule a pick-up, apply a balance, etc. – and help guide the customer journey – show similar items in stock that match a customer’s stated preferences, find complementary items, show updated loyalty points, etc. 

 Successfully deputizing AI agents into customer data management and personalization will require agents to have access to the unified customer profile. As consumers become more comfortable with agentic AI as a CX tool, we may even begin to see a time when consumers create their own personal agents for different brands – with more trusted brands receiving more detailed data and preferences from the customers’ agents. Agentic AI may become a two-way street in other words. Brands that are open with how they use agentic AI to improve CX may be rewarded by customers providing more data through their own agents, which will then further improve CX. The key component in successfully integrating agentic AI into CX is high-quality data. Organizations that prioritize data readiness will discover that an accurate, real time understanding of a customer is the backbone to power any emerging CX use case.

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