In today’s fiercely competitive market, delivering personalized experiences is crucial for customer engagement and loyalty. However, marketers who fail to adapt to the transition from decision-tree to trigger-based engagement strategies risk falling behind and missing out on the opportunity to captivate customers at every touchpoint with hyper-personalized experiences. 

In this workshop, discover how to seamlessly collaborate with your data team to unlock the full potential of your first-party data and create personalized experiences that captivate your customers at every touchpoint. 

Register and attend “How To Power Next Best Action with First-Party Customer Data,” presented by Snowplow.

Click here to view more MarTech webinars.

The post Power next-best action with first-party data appeared first on MarTech.

Research shows that businesses make up to three billion decisions annually and a recent survey by Gartner reported that 65% of decisions are more complex (involving more stakeholders or choices) than they were two years ago.

Many businesses today, and the marketers that serve them, need better insight to bridge the gap between massive amounts of data and business decisions. Only 24% of companies say they are “data-driven,” whereas others face missed opportunities, inefficiencies, and increased business risks. The average S&P company loses $250 million annually due to poor decision-making.

Decision intelligence is a framework that bridges the gap between insights and decisions. It empowers organizations to make better, consistent, and data-driven decisions. Leaders and teams can make informed decisions at every level of the company!

What is decision intelligence?

Decision intelligence (DI) is an evolving discipline that combines data, analysis, AI, automation, and experience to make better decisions. DI helps guide decision-makers with actionable insights using optimization, simulation, and decision-analysis techniques.

In contrast to traditional decision-making approaches, which rely heavily on intuition and experience, DI incorporates methodical, analytical, and data-driven approaches.

The focus of DI is not just on the technology but on how it augments human decision-making processes. It is a multidisciplinary field drawing on expertise from various arenas, including computer science, statistics, psychology, economics, and business.

According to Dr. Loren Pratt, chief science officer and co-founder of DI software provider Quantellia, and author of “How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World,” another key concept of DI is designing decisions like organizations design homes, buildings, and airplanes — by creating a blueprint first.

Much like a blueprint, a decision design helps align everyone involved in that decision — including stakeholders — around its rationale. She found that by treating decisions like a design problem, you can bring many design best practices to bear, such as ideation, documentation, rendering, refinement, QA, and design thinking.

In 2019, Google’s first Chief Decision Officer, Cassie Kozyrkov, established a new decision intelligence engineering discipline to augment data science with behavioral science, economics, and managerial science to focus on the next business advantage beyond the data.

Intelligent decisions are designed, simulated, automated, monitored, and tuned. 

Dig deeper: Why data-driven decision-making is the foundation of successful CX

What decision intelligence is not

Decision science. Decision science has usually been associated with the qualitative side of data. DS is the overarching term, while “decision intelligence” is the operational side. 

Strategic intelligence. Broadly, strategic intelligence means using BI insights to drive and support strategy. We also call this market intelligence which provides businesses with current industry trends and makes sense of consumer behavior to navigate a future course of action.

Calculated decisions. Not every output or recommendation is a decision, Kozyrkov says. In decision analysis terminology, a decision is only made after an irrevocable allocation of resources takes place. If you can change your mind for free, no decision has yet been made.

Applications of decision intelligence

DI applies to various decision-making problems, such as resource allocation, risk management, strategic planning, and, yes, marketing. I’ve used it in developing systems and platforms for complex energy, finance, policy, and marketing decisions.

Our last startup platform supported DI for go-to-market executives reducing the decision-making process from nine months to a fraction of time with greater visibility, training, and impacts.

DI has been applied in credit applications or fraud detection in financial services.  It has been used in retail to determine how much inventory to purchase, optimal stock levels, or price forecasts. According to Dr. Loren Pratt, employing decision intelligence can positively impact evidence-based decisions in a healthcare crisis.

Other use cases include customer satisfaction, marketing attribution, and competitive and go-to-market strategies. Designs of the framework of these decisions were standard for GTM; however, implementation required building an enterprise platform, training, and data support. But in the end, this decision-making time dropped from nine to one-to-three months. The average impact was over $10 million, including an apparel company discovering a new $90 million revenue stream embracing the platform. 

Dig deeper: Automating decisions with real-time situational context

Benefits of decision intelligence

McKinsey senior partner Kate Smaje states that organizations are now accomplishing in 10 days what used to take 10 months. Having DI supports the continually increasing pace of decisions required to stay competitive.

The first benefit is DI aids leaders in navigating complex decisions with more focused and comprehensive information. As you design the decisions, you can structure cross-organizational information toward specific goals or objectives. Having this kind of visibility facilitates navigating trade-offs between competing objectives. It eliminates more of the analysis paralysis found in most strategic and high-level tactical decisions. 

Next, DI reduces risk and uncertainty. Decision-makers with real-time data and insights can leverage DI to identify and proactively mitigate potential risks. With the visibility in trade-offs, organizations can better apply risk/reward plans to avoid costly mistakes hindering a competitive edge.

Decision Intelligence enhances efficiency and productivity. By automating specific decision-making processes and providing decision-makers with real-time data and insights, DI can help streamline decision-making and improve productivity. You are reducing decision latency. These processes can be built or programmed into systems to free up time and resources to explore more options or allocate to other important tasks and initiatives.

Finally, organizations leveraging DI gain a more potent competitive edge by leveraging data and technology by evaluating, then acting on, more intelligent and faster complex decisions which typically cripple momentum or transformation.

Limits and challenges of decision intelligence

With data, AI, and automation involved, it’s not surprising that there are some challenges and limitations that are also present with DI.

Ethics/bias. DI can methodically help reduce bias and reinforce ethical decisions. At the same time, with any data-driven and automated system, decisions leveraging DI built by humans still risk being developed based on biased or discriminatory data or algorithms. Awareness training, along with all other organizational data-driven efforts, is a must.

Data availability. Leaders and project managers must be aware of data access and availability limitations. Decision effectiveness is often challenging to find on smaller datasets. Sometimes things go wrong, but it’s more based on luck than data. For complex and infrequent decisions, an organization may need help to define an approach for measuring decisions. In such cases, technology limitations may prevent a solution. Organizations need to formalize such decision-making processes and can only use technology. Also, it’s worth highlighting what could be missing or the scope of what’s possible.

Resistance. An important part of DI is ensuring more transparency, consistency, and training in the decision-making process. The traditional culture of decision-makers will initially be resistant as it feels that it dismisses their experience or instinct or runs against their specific agendas. Those in charge of DI efforts need to communicate how DI benefits their efforts and leads to better outcomes for individuals and organizations.

Leaders can overcome these challenges and limitations through clear communication and a well-defined scope of its application. Each new initiative can grow and enhance an organization’s decision-making culture.

Tips and factors

• Choose a focused decision. Begin by implementing DI in functions where business-critical decision-making needs improvement (e.g., data-driven, AI-powered). Alternatives include large complex decisions or ones that can be scaled and accelerated through automation.
  
• Begin with outcomes. There’s a flood of data in your organization, but you should only gather relevant data to that outcome to design a decision model. Add additional data or test theories of additional information once you’ve started with your early set.

• Map out decisions. Document assumptions, thoughts, emotions, concerns, and fears involved in your decisions. Review them quarterly or semi-annually. It will increase your organization’s decision-making muscle.

• Don’t automate everything. Humans, especially when it comes to complex and sensitive decisions, are necessary.

• Authority should be to the decision. Provide authority to make decisions to the people closest to the point of impact of that decision. Ownership will incentivize effective decision-making.
  
• Develop new decision-making habits. Teach decision-makers to apply systematic best practices, such as critical thinking, trade-off analysis, recognizing bias, and listening to opposing views.

• Beware narrow framing. In the book “Decisive” by Chip and Dan Heath, the authors explain that a straightforward way to improve decision-making is to avoid limiting the scope of the frame. A decision is rarely just a “yes” or “no.” There are always multiple options, so have at least three available for any decision.

Conclusion

Decision-makers frequently need more information, time, and experience to make complex decisions. A study by Bain found that business performance seems 95% correlated to the effectiveness of decisions. Decision intelligence systems improve efficacy by explaining and justifying the decisions, learning from past decisions’ feedback, and comparing the impact to improve decision effectiveness.

Decision intelligence is a crucial tool that can help you make better decisions. By combining data science, AI, and human expertise, DI can help reduce uncertainty and improve effectiveness. However, DI has its challenges and limitations. You must be aware of these risks and take steps to mitigate them.

The post How to use decision intelligence to tackle complex business challenges appeared first on MarTech.

“As the industry demands measurement that is trusted, independent and founded on real viewing from real people, we continue to support the MRC guidelines that set the standard for quality, audited measurement,” Karthik Rao, CEO, Audience Measurement at Nielsen, said in a statement. “It’s our daily mission to maintain our methodologies at the highest standard so that our clients can trade with confidence well into the future.”

Why we care. The suspension was a good thing for marketers in many ways. For nearly all of the broadcast era, Nielsen had what was essentially a monopoly on measuring ratings. For much of that time, both TV networks and advertisers complained about the accuracy of the data. Improving the quality of those numbers means brands are less likely to be paying for audience they aren’t getting.

Also, it has opened the door to competitors. NBCUniversal, Paramount and Warner Bros. Discovery have all announced they are working with other data providers, including Comscore Inc. and startups such as VideoAmp, iSpot.tv and EDO. More competition means better service.

Dig deeper: Nielsen announces first module for cross-screen audience measurement platform

What happened. The MRC suspended Nielsen’s accreditation in September 2021 for two reasons. First, an investigation by the council found the company undercounted TV viewers during the pandemic because technicians were not able to get into panelists’ homes to fix devices. Second, Nielsen reported a software error had caused it to undercount out-of-home viewership for nearly six months.

Bad timing. The suspension came amidst an ongoing drop in TV viewership which made Nielsen’s ratings less valuable. Since 2011 major network broadcast ratings have dropped more than 80%, according to SpoilerTV. Further, the cord-cutting trend continues apace. The share of Americans who say they watch television via cable or satellite has plunged from 76% in 2015 to 56% in 2021, according to a Pew survey.

The post Nielsen’s national TV ratings gets accreditation back after 19-month suspension appeared first on MarTech.

The predicted increase of over 30% is derived from a survey of 200 U.S. and European marketers, as well as interviews with industry experts.

Why we care. These findings underline the extent to which marketing will become increasingly data-driven in the months and years to come. In order to be data-driven at scale, marketing will rely more and more on an agile and well-integrated martech stack.

What’s more, the range of skills required within marketing organizations — or in close proximity — will have to include confidence in handling analytics and data.

Major challenges. Despite these prospects for growth, Winterberry listed four major challenges that continue to dog the data and analytics space:

• Quality of data and persistence of data silos.
• Conducting measurement despite “black boxes” (unreliable vendor performance data).
• The deprecation of third-party cookies.
• A shortage of talent.

Dig deeper: Marketing analytics: What it is and why marketers should care

A work in progress. Many marketing organizations recognize themselves as lacking maturity when it comes to data and analytics. 47% describe themselves as “emerging” or “progressing”: only 10% consider themselves “leaders.”

The main areas of interest as organizations develop the right muscles are audience intelligence, the customer journey, customer experience, creative and content, and media measurement and attribution.

“Organizations leading the charge in analytic decision-making have demonstrated that a cohesive strategy across data, technology, people, and processes is key to success,” said Michael Harrison, Winterberry Group Managing Partner, in a release.

The post Spend on marketing analytics and data infrastructure to grow sharply appeared first on MarTech.

Attracting interest in a tool or product may be the easy and fun part of building a business, but converting inquiries to purchasers is when it gets hard. So many brands today continue to struggle with acquiring, converting, retaining and creating loyal customers for life. Yet leading companies across the globe seem to be accomplishing all of that and then some.

Want to know how? Register and attend “Harnessing the Power of Customer Data to Stay Ahead of the Competition,” presented by Tealium.

Click here to view more MarTech webinars.

The post Harness the power of customer data to stay ahead of the competition appeared first on MarTech.

What are you hoping to get out of your marketing technology? Do you need help getting there?

At the heart of marketing technology, customer data platforms offer marketers and advertisers a powerhouse of capabilities to drive relevant, personalized customer experiences. And at the heart of the CDP lies the data that fuels customer insights. 

Learn more by registering and attending “Get to the Heart of Customer Experience With Data that Delivers,” presented by Salesforce and Acxiom.

Click here to view more MarTech webinars.

The post Drive better customer intelligence for a better customer experience appeared first on MarTech.

Researching identity resolution vendors

Once you determine an enterprise identity resolution platform makes sense for your business, spend time researching individual vendors and their capabilities by doing the following: 

• Create and prioritize a list of identity resolution use cases, from essential to not necessary. 
• Use that list as a basis for your research — many of the vendors profiled in this report also provide blogs, ebooks and interactive tools that can help. 
• Make a list of the vendors meeting your criteria, reach out to them and set a deadline for replies. 
• Decide whether or not you need to engage in a formal RFI/RFP process.

Identity resolution is not only critical to marketing success but is essential for compliance with consumer privacy laws such as CCPA and GDPR. Explore the platforms essential to identity resolution in the latest edition of this MarTech Intelligence Report.

Click here to download!

RFI/RFP process

The RFI/RFP process is an individual preference, however be sure to give the same criteria to each vendor to facilitate comparison. The most effective RFPs only request relevant information and provide ample information about your brand and its identity resolution needs. It should reflect high-level strategic goals and KPIs. For example, mention your company’s most important KPIs and how you will evaluate the success of your efforts. Include details about timelines and the platforms in your existing martech stack. 

When written properly, an RFP will facilitate the sales process and ensure everyone involved comes to a shared understanding of the purpose, requirements, scope and structure of the intended purchase. From the RFP responses, you should be able to narrow your list down to three or four platforms to demo.

Demo the platforms

Schedule demos as close together as possible for the best comparisons. Make sure all potential users are on the demo call and pay attention to the following: 

• How easy is it to use? 
• Does the vendor understand our business and marketing needs? 
• Are they showing us our “must-have” features?

Questions for vendors

Here are some questions to ask vendors that touch on important considerations in your identity resolution search:

Data onboarding and privacy 

• Does the platform support first-party data onboarding? 
• Can we incorporate any of our private customer IDs into the platform? 
• Do you use probabilistic, deterministic or a hybrid approach to matching? 
• How do you validate the accuracy of your deterministic matches? 
• What match rate can we expect, given our vertical market and database size? 
• How do you comply with privacy regulations and consumer choice? 

Identity graph 

• Do you own or license your referential identity data? 
• What are your identity data sources? 
• How do you validate the quality of your identity graph? 
• How much of your data is addressable? 
• How is your identity graph linked to offline PII? 
• Do your identity capabilities apply to non-U.S. markets? 

Martech and adtech integration 

• How does the platform integrate with martech platforms (i.e., CRMs, DSPs, CDPs)? 
• Does the platform feature any built-in data activation capabilities (i.e., personalized email or ad campaign execution)? 
• Do you have APIs available for data import/export? 
• What reporting do you provide that will document the ROI from our identity efforts? 

Customer support 

The post 24 questions to ask identity resolution vendors during a demo appeared first on MarTech.

Inevitably, this begs the question: should you employ your existing DWH as a customer data platform (CDP)? After all, when you re-use an existing component in your stack, you can save resources and avoid new risks.

But the story isn’t so simple, and multiple potential design patterns await. Ultimately, there’s a case for and against using your DWH as a CDP. Let’s dig deeper.

DWH as a CDP may not be right for you

There are several inherent problems with using a DWH as a CDP. The first is obvious: not all organizations have a DWH in place. Sometimes, an enterprise DWH team does not have the time or resources to support customer-centered use cases. Other enterprises effectively deploy a CDP as a quasi-data warehouse. (Not all CDPs can do this, but you get the point.)

Let’s say you have most or all your customer data in a DWH. The problem for many, if not most, enterprises is that the data isn’t accessible in a marketer-friendly way. Typically, an enterprise DWH is constructed to support analytics use cases, not activation use cases. This affects how the data is labeled, managed, related and governed internally. 

Recall that a DWH is essentially for storage and computing, which means data is stored in database tables with column names as attributes. You then write complex SQL statements to access that data. It’s unrealistic for your marketers to remember table and column names before they can create segments for activation. Or in other words, DWHs typically don’t support marketer self-service as most CDPs do. 

This also touches on a broader structural issue. DWHs aren’t typically designed to support real-time marketing use cases that many CDPs target. It can perform quick calculations, and you can schedule ingestion and processing to transpire at frequent intervals, but it is still not real-time. Similarly, with some exceptions, a DWH doesn’t want to act off raw data, whereas marketers often want to employ raw data (typically events) to trigger certain activations.

Finally, remember that data and the ability to access it don’t maketh a CDP. Most CDPs offer some subset of additional capabilities you won’t find in a DWH, such as:

• Event subsystem with triggering.
• Anonymous identity resolution.
• Marketer-friendly interface for segmentation.
• Segment activation profiles with connectors.
• Potentially testing, personalization and recommendation services.

A DWH alone will not provide these capabilities, so you will need to source these elsewhere. Of course, DWH vendors have sizable partner marketplaces. You can find many alternatives, but they’re not native and will require integration and support effort. 

Not surprisingly, then, there’s a lot of chatter about “composable CDPs” and the potential role of a DWH in that context. I’ve argued previously that composability is a spectrum, and you start losing benefits beyond a certain point. 

Having issued all these caveats, a DWH can play a role as part of a customer data stack, including:

• Doing away with a CDP by activating directly from the DWH. 
• Using the DWH as a quasi-CDP with a reverse ETL platform.
• Coexisting with a CDP.

Let’s look at these three design patterns.

1. Connecting marketing platforms directly to your DWH

This is perhaps the most extreme case I critiqued above, but some enterprises have made this work, especially in the pre-CDP era and platforms (like Snowflake with its broad ecosystem) are looking to try to solve this.

The idea here is that your engagement platform directly connects to push-pull data with a DWH. Many mature email and marketing automation platforms are natively wired to do this, albeit typically via batch push. Your marketers then use the messaging platform to create segments and send messages to those segments in the case of outbound marketing.

Imagine you had another marketing or engagement platform, a personalized website or ecommerce platform. Again you draw data from DWH, then employ the web application platform to create another set of segments for more targeted engagement.

Do you see the problem yet? There are two sets of segmentation interfaces already. What happens if you had 10 marketing platforms? 20? You will keep creating segments everywhere, so your omnichannel promise disappears. 

Finally, what if you had to add another marketing platform that did not support direct ingestion from a DWH?

2. Employ DWH with reverse-ETL tools

This approach solves several problems with the first pattern above. Notably, it allows (in theory) a non-DWH specialist to create universal segments virtually atop the DWH and activate multiple platforms. With transformation and a better connector framework, you can apply different label mappings and marketer-friendly data structures to different endpoints.

Here’s how it works. Reverse ETL platforms pull data from the DWH and send it to marketing platforms after any transformation. You can perform multiple transformations and send that data to several destinations simultaneously. You can even automate it and have exports run regularly at a predefined schedule.

But a copy of that data (or a subset of it) is actually copied over to target platforms, so you really don’t have just a single copy of data. Since the reverse-ETL platform does not have a copy of data, your required segments or audiences are always generated at query time (typically in batches). Then you export them over to destinations.

This is not a suitable approach if you want to have real-time triggers or always-on campaigns based on events. Sure, you can automate your exports at high frequency, but that’s not real-time. As you increase your export frequency, your costs will exponentially increase.

Also, while reverse-ETL tools provide a segmentation interface, they tend to be more technical and DataOps-focused rather than MOps-focused. Before declaring this a “business-friendly” solution suitable for marketer self-service, you must test it carefully.

3. DWH co-exists with CDP

Your enterprise DWH serves as a customer data infrastructure layer that supplies data to your CDP (among other endpoints). Many, if not most, CDPs now offer some capabilities to sync from DWH platforms, notably Snowflake.

There are variations in how these CDPs can co-exist with DWH. Most CDPs sync and duplicate data into their repository, whereas others (including reverse-ETL vendors) don’t make a copy. However, there could be trade-offs you need to consider before finalizing what works for you.

In general, we tend to see larger enterprises preferring this design pattern, albeit with wide variance around where such critical services as customer identity resolution ultimately reside. 

Dig deeper: Where should a CDP fit in your martech stack?

Wrap-up

DWH platforms play increasingly essential roles in martech stacks. However, you continue to have multiple architectural choices about which services you render within your data ecosystem.

I think it’s premature to rule out CDPs in your future. Each pattern has its trade-offs to keep in mind while evaluating your options. 

The post Should you use your data warehouse as your CDP? appeared first on MarTech.

Does our customer data reside in disconnected silos throughout the organization?

Organizational silos between departments such as sales, marketing, procurement or customer support can lead to inconsistent customer experiences with a brand. An identity resolution platform can connect these systems. It will integrate consumer identifiers across channels and devices in a way that is accurate, scalable and privacy compliant to create a persistent and addressable individual profile.

Do we have customer knowledge gaps that could be filled with trusted second- and third-party data?

First-party data is essential for building a strong relationship between your brand and customers. However, identity graphs using anonymized second- and third-party data can provide valuable demographic, location, financial and other information that can fill gaps in customer insights. As data collection and matching techniques improve, creating a 360-degree view of customers through identity resolution platforms may make sense.

Are we in compliance with CCPA, GDPR and other data privacy regulations?

Data breaches and misuse of consumer data continue to make headlines, leading to an increase in privacy regulations. It’s crucial to ensure your data governance practices comply with the EU’s General Data Protection Regulation (GDPR) and/or the California Consumer Privacy Act (CCPA). While collecting and using consumer data is an essential part of marketing, it also escalates the risk of damaging your brand and incurring legal consequences.

Can we successfully integrate our existing customer data systems with an identity resolution platform?

Your various martech and ad tech systems absolutely must be able to communicate with each other. If they can’t, your organization likely would benefit from an identity resolution platform. This platform can incorporate identifiers and profiles between and within these systems for consistency and accuracy, creating a persistent and addressable individual profile

Does our C-suite support identity resolution initiatives?

Most C-level executives overestimate their marketing organization’s customer identity accuracy and persistence, according to a Forrester study. This can lead to inadequate budgeting, campaign measurement and performance, and broken customer experiences. Therefore, it is critical to secure C-suite support for identity resolution initiatives across the organization.

Identity resolution is not only critical to marketing success but is essential for compliance with consumer privacy laws such as CCPA and GDPR. Explore the platforms essential to identity resolution in the latest edition of this MarTech Intelligence Report.

Click here to download!

How would we use identity resolution?

Identity resolution has many marketing use cases, from complying with data privacy regulations to developing more accurate lookalike audiences to improved marketing segmentation and targeting. Identifying the use cases that would most benefit your organization is fundamental for establishing and prioritizing the capabilities you’ll need.

What KPIs do we want to measure and what decisions will we make based on the data?

It’s critical to measure the impact of an identity resolution platform on your marketing ROI. Resolving customer identities will provide new cross-sell and upsell opportunities because your marketing team knows more about your customers. Although KPIs vary by organization and/or industry, you should be able to measure incremental lift in metrics such as average order value, average revenue per user, basket size, response rates or customer retention.

What is the total cost of ownership?

Most of these platforms use on-demand pricing, meaning customers pay a monthly subscription price that will vary by usage. Pricing is typically based on the number of data records or customer profiles under management or the number of matches or API calls. Some also have add-on customer support options.

The post Does your organization need an identity resolution platform? appeared first on MarTech.

Data analysis is supposed to replace hunches with facts. Brands don’t want to risk millions of campaign dollars on someone’s gut instinct. The marketer, ideally, has a goal, a clear threshold of success that must be crossed to achieve results. So how do you get there?

Data analysis is the “GPS.” The whole point of data analysis is to understand what is going on and to use that information to make the right decision. It’s “ready, aim, fire” (data, analysis, action). But sometimes the order gets mixed up, resulting in people drawing the wrong conclusions and acting on that basis. The process then becomes “ready, fire, aim”, or even more comically, “fire, aim, ready”.

“The biggest test of data is analytics,” said Mark Stouse, chairman and CEO of Proof Analytics.  “It contextualizes the data, making it extraordinarily difficult to manufacture conclusions, whereas data visualization alone makes it easy.”

Can data identify what’s causing something?

Can one gauge causality from data alone? Stouse believes not. Marketers can try by extrapolating from historical data, then check to see if this extrapolation was correct. “If everything is stable, extrapolation can work. But when the variety, volatility and velocity of change is great, extrapolation has zero value.”

“Data is indeed always about the past, and it has no innate ability to forecast. Past is not Prologue,” he continued. “But multivariable regression is the proven approach to taking data representing the relevant factors (the known knowns) — as well as some potentially important stuff (known unknowns) — and turning that into a calculated historical portrait of causality. That, in turn, creates a forecast against which you can understand the accuracy of the model vis-à-vis a comparison between forecast and actuals.”

Erica Magnotto, director of SEM at Accelerated Digital Media, sees the value of historical data, but only if there is room for retroactive perspective and predictive planning. “Forecasting campaign success should be based on trending data and performance like year-over-year and month-over-month. This should create close to accurate predictions on future success. If the forecasted data indicates a slower month or potential downturn in the market, optimizations can be made in real time to promote efficiency and conservative scale. If forecasting indicates a stronger month, then it’s time to start planning for scale, testing and additional campaign launches.”

Marketers should also be aware of hiccups in the model. Magnotto noted that there is a difference between normal “ebb and flow”’” of performance versus a crash/spike. “Data occurring outside of the normal margin of ebb and flow could indicate that immediate action in the account is necessary. Marketers should also not assume user behavior will always be consistent so it’s important to understand benchmark performance so abnormal user (or campaign) behavior can be detected,” she said.

Dig deeper: Marketing analytics: What it is and why marketers should care

What can marketers do?

Marketers must be analytical, open-minded, and humble at the same time. This alone can be a challenge when there are always some people who can be too self-assured, or fixated on the trivial at the expense of the substantive. Still, there are approaches to check mistakes before they happen.

Magnotto focused on knowing the data, the customer, and acknowledging reality. She offered this checklist for agencies, but the main points on it apply to brands too:

1. Understand basic excel/sheets principals and how to pivot large sets of data downloaded from any platform. 

2. Understand basic comparison formulas and default ways to look at data trends (month-over-month, year-over-year, period-over-period, week over week).

3. Have agreed upon primary KPIs and secondary KPIs with the client. 

4. Always speak the client’s language and incorporate the client’s source of truth data into reporting. This will ensure more productive conversations and help marketers navigate away from making mistakes or misreading performance. 

5) Know when to admit defeat in a campaign strategy. If a “great idea” is not working, then be comfortable allowing the data to speak for itself and changing strategies. 

6) Always QA reporting. Apply QA to formulas, timeframes, numbers, etc. If something looks too good to be true when analyzing data, it probably is. QA for mistakes that may be leading to that anomaly. 

Stouse stressed avoiding a fixed mindset. “Blindness to analytical reality is about choosing not to see, because what is there offers a challenge to what you believe.” he said. “The opposite of analysis is a certainty you have chosen and justified without any real basis except your own self-interest.  More mistake have been made in the name of certainty than anything else I can think of.”

The post Data plus analytics is the route to the truth appeared first on MarTech.