Mobile, AI Highlight E-mail Marketing Trends for 2020

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AccuList’s e-mail list and data services clients can expect to ride some positive trends into 2020, according to e-mail marketing pros. While continuing to top other channels in terms of ROI, e-mail marketing worldwide in 2019 also showed improved delivery rates, rising open rates and increasing click-through-rates, per data from UK-based Data and Marketing Association. Digital marketing platform Smart Insights recently surveyed experts to find ways to further leverage the positives this year.

Mobile-Optimize E-mail to Target Lead Source of Opens

The impact of mobile usage on e-mail can’t be underestimated. In 2019, mobile browsing (53% of traffic) surpassed desktop/tablet browsing (47% of traffic), notes Smart Insights. And while mobile still lags in terms of purchase revenue (32%) versus desktop/tablet (68%), it’s growing fast, with a 23% year-over-year increase that has helped accelerate mobile optimization across digital channels. Data from Litmus shows that mobile devices also led in e-mail opens (41.9%) compared with desktop opens (18.2%) by Q1 of 2019. Clearly, mobile optimization is a priority for e-mail marketers. Coding for a mobile-viewing format is only the beginning. Changes to copy, images and overall layout can boost click-through by streamlining presentation to place the focus on quick engagement and links to landing pages, product pages, blogs, etc., note experts. Concise copy with enough white space for easy reading is a first goal. Other tips include using image dimensions that are small enough to render well and placing images on the “first fold” to encourage scrolling. Using e-mail design with short copy not only leads to quick scanning but also allows the call-to-action (CTA) to be seen early and avoids excess scrolling that loses click-throughs. Remember that the surge in wearable technologies is limiting even more of the space marketers have for messaging. Finally, make sure that e-mails are rendering correctly across all types of devices, a testing option offered by most e-mail service providers.

Watch for AI to Expand Potential Use in E-mail Marketing

Artificial Intelligence (AI) is coming of age in marketing, including e-mail campaigns. Worldwide, 30% of companies will be using AI in at least one sales process in 2020, Gartner predicts, and 87% of current AI users say they are planning on using it for sales forecasting and e-mail campaign enhancement, per Statista. Connext Digital foresees seven ways that AI will begin to influence e-mail marketing: 1) applying algorithms and data insights for predictive personalization; 2) analyzing demographics, purchasing patterns and online behavior for smarter segmentation; 3) automating workflow for e-mail triggering, tailored messaging and lead nurturing; 4) using natural language technology to find the best words for response optimization of subject lines and CTAs; 5) improving e-mail timing and frequency by specific recipient;6)  A/B and multivariate testing to quickly identify trends and develop predictive results; and 7) developing data to enhance broader analyses, for example to predict potential churn.

Facing Privacy Laws, E-mail Marketers Focus on Building Trust

Cold e-mailing, spamming and phishing have tainted e-mail’s reputation among consumers (and marketers), but recent privacy regulations, including the European Union’s GDPR and California’s CCPA, have spurred the e-mail marketing world to go beyond CAN SPAM to focus on privacy, compliance and subscriber trust. E-mail definitely continues as a strong communications channel despite past abuses. A 2019 Drift study found that e-mail is the leading communications channel for B2C, far above websites, social media, and even face-to-face meetings, with 65% of respondents saying that they used e-mail to communicate with organizations in the past 12 months. Compare that with 55% communicating via telephone and 42% via websites, the next most popular channels. So with the majority of recipients only receiving e-mails to which they have opted-in from particular senders, smart marketers will want to focus on building on recipients’ trust and providing value in 2020.

Read more detail from Smart Insights.

Prep for 2020 Marketing With Clean, Personalized, Predictive Data

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As 2019 closes, AccuList’s data services clients have a year’s worth of multichannel customer, campaign and sales information to analyze and inform 2020 plans. So what are the big trends that the data pros foresee will deliver maximum ROI?

Data Hygiene Issues Remain a Priority

Clean, up-to-date, quality data is still the basis for good marketing analyses and campaign planning. A November Business2Community post by marketer Dan Moyle helpfully summarized the key data cleansing tasks that businesses need to undertake to hit the ground running in 2020. After all, it’s estimated that 20% of the average contact database is dirty, so this is not a trivial effort. Increasing marketing efficiency, response and customer loyalty, requires removing data errors and inconsistencies. Start by monitoring data for issues such as duplicates, missing information or bad records to figure out how and where they are occurring. Then standardize processes at each entry point. Next validate the accuracy of data across the database by investing in data tools or expert data services, and commit to regular cleansing and maintenance of data quality. Identify and scrub duplicates. Once the data has been standardized, validated and de-duped, improve its analytic value by using third-party data appending sources (to flesh out demographics, psychographics, firm-ographics, purchase history, etc.) for a more complete customer picture. Establish a feedback process to spot and update, or purge, incorrect information, such as invalid e-mail addresses identified by a campaign. And communicate standards and processes to the whole team so that they understand the value of clean data in segmentation targeting, lead response, customer service and more.

Using Data for an Agile, Personalized, Customer-Centric Edge

Data trends figured prominently in the 2019 Martech Conference and a recent article from martech firm Lineate highlights a few keynotes, such as the role of data in personalization. When a 2019 RedPoint Global survey of U.S. and Canadian consumers finds that 63% expect personalization as a standard of service and want to be individually recognized in special offers, personalized marketing is clearly a competitive essential. Expect to see use of Artificial Intelligence (AI) and machine learning (ML) increase in 2020 as personalization tools. Machine learning is when a computer is able to find patterns within large amounts of data in order to improve or optimize for a specific task. For example, for more personalized offers and messaging in acquisition, this means using ML to recognize if people from certain areas are more likely to respond to a specific offer or which past high-response special offers may resonate in future . Personalization is also key to the customer-centric experience proven to drive long-term retention and brand loyalty–as opposed to getting the same message again and again. When personalization is combined with elimination of data silos and creation of a single customer view across channels, marketing becomes especially powerful. Indeed, integrated database development and the elimination of data silos are also key to the growing “agile marketing” trend. Agile marketing breaks down team silos (which assumes breaking down data silos) in favor of teams focusing on high-value projects collectively. According to a 2018 survey by Kapost, 37% of businesses have already adopted agile marketing, and another 50% said they haven’t yet become agile but expect to be soon.  

Taking Data Insights From Retroactive to Predictive

Looking ahead to 2020, marketers should also consider adding predictive modeling to their toolkit if they haven’t already done so. Why? A study by ClickZ and analytics platform provider Keen found that 58% of marketers using predictive modeling experienced a 10%-25% ROI lift, while another 19% saw more than a 50% uplift. While retroactive campaign data can be very useful for reporting and results analysis, it’s not always as good for informing future multichannel directions, for optimizing media investments, or for quick execution and performance assessment. In fact, nearly 80% of Keen/ClickZ survey respondents felt they’d missed opportunities because of slow or inaccurate decision-making using non-predictive data reporting. For example, standard data analysis often fails to span all channels (e.g., online video vs. store-level programming) and mistakenly gives most credit to last-click channels such as search or transactional activities. In contrast, the Keen/ClickZ survey found marketers using predictive modeling boosted results in multiple areas, including a better understanding of the target audience (71%), optimizing of touchpoints on the customer journey (53%), and improving creative performance (44%). Predictive modeling also can help businesses synthesize large volumes of data, a key concern for many; in fact, 38% indicated their current measurement solutions do not support the scale of their data.

 

Research Shows ABM, AI, Analytics Drive B2B Marketing Success

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A new report based on business-to-business marketing data from Salesforce Research, Forrester Research and the Information Technology Services Marketing Association shows how technically sophisticated top-performing B2B marketers have become in order to woo today’s demanding clients. “B2B marketers are increasingly using a mix of account-based marketing (ABM), artificial intelligence (AI), and analytics to connect the right customers with the right content at the right moments,” concludes B2B Marketing Trends: Insights From the Frontline released in June. To enlighten our B2B clients, AccuList can pass along a few key findings.

Unified Data Vital to Personalization Demand

Today’s business buyers demand personalization: 69% of business buyers expect companies to anticipate their needs, and 60% of business buyers are comfortable with companies applying relevant personal information in exchange for personalized engagement. B2B marketers are not quite up to speed yet, however, with only 46% of B2B marketers reporting a completely unified view from customer data sources. This is true even though most marketers agree that personalization improves brand building (92%) and customer advocacy (80%). The high-performing marketers have invested in customer data and are reaping the rewards, with 66% of high-performing teams saying they are satisfied with their ability to use data to create relevant, personalized experiences. In contrast, the under-performers are way behind, with only 7% satisfied with their use of data.

High-Performing Marketing Teams Use ABM

Account-based marketing (ABM) programs are collaborative efforts between marketing and sales teams, designed to focus attention on high-value customer accounts. High-performing B2B marketing teams are much more likely to collaborate effectively with sales teams on ABM programs (54%) compared with under-performing marketing teams (34%), according to the report. Because of the value of ABM programs, one-third of B2B marketers are currently planning to build them into their existing marketing automation platforms. Among B2B marketers using ABM, the ABM programs now account for more than a quarter of their total marketing budgets. Why? Nearly half of ABM users say the programs deliver higher ROI than comparable marketing methods: 77% of ABM users are achieving 10% or greater ROI, and 45% of ABM users are seeing at least double ROI compared to other marketing methods. ABM ROI is not a slam-dunk however; the top four challenges reported include getting data and reports to track results, personalizing marketing to key account contacts, getting adequate budget to support programs and resources, and developing customizable, scalable campaign assets. To further leverage ABM, many marketers have added, or plan to add, technology platforms such as website personalization to serve relevant content, predictive analytics to select accounts, and business intelligence or ABM data aggregators to measure results by account, etc. Also gaining in popularity is use of chatbots or conversational interfaces, while traditional efforts such as personalized, dimensional direct mail integrated into digital marketing continues to bolster ABM, too.

Growing Use of AI by B2B Marketers

Some 69% of business buyers expect personalized “Amazon-like” customer experiences today, per the recent B2B report. As a result, AI usage among B2B marketers grew 23% in 2018, with the majority of these marketers using AI within marketing platforms to optimize mid-cycle engagement. B2B marketers are using AI to facilitate online experiences with offline customer data, to drive next best offers in real time, to improve customer segmentation, to create dynamic websites and landing pages, and to personalize overall customer journeys, as well as a number of other goals. B2B marketers are also beginning to use AI technology beyond their marketing automation platforms; for example, almost half of B2B marketers use connected devices, and one-third added voice-activated personal assistants (such as Apple’s Siri and Amazon’s Alexa) in 2018. Register to download the free “B2B Marketing Trends: Insights From the Frontline” for more data on other B2B marketing trends.

Avoid Segmentation Missteps to Boost List ROI

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List segmentation is key in targeted direct marketing, which is why the AccuList team offers clients help in defining best-performing customer segments via predictive analytics services and data management services. Over the years, we’ve learned that the secret to success is as much a matter of strategic mindset as technical expertise. A recent MarketingProfs article by Mitch Markel, a partner in Benenson Strategy Group, makes that point by identifying some of the common strategic errors that can trip up a segmentation effort.

Obvious Parameters and Old Strategies Dig a Rut

Marketers need to be aware that segmentation models can slip into an ROI rut. Use of obvious profiling parameters and assumptions is one reason. Certainly, demographics (or firmographics), stated needs, and past purchase behavior are essential in grouping for likely response and lifetime value, but people don’t make decisions solely based on these factors. Markel urges research that also looks at fears, values, motivations and other psychographics in order to segment customers or prospects not just as lookalikes but also as “thinkalikes,” which can be especially helpful in crafting personalized content and messaging. Markel cites the examples of car buyers grouped by whether they value safety over performance, and food purchasers sorted for whether they stress healthy lifestyle or convenience. Past success is another reason segmentation can get stuck in a rut. Because segmentation requires an upfront investment, marketers tend to want to stick with proven targeting once the segmentation study is completed. But today’s hyper-personalized, digital environment has accelerated the pace of change in markets, perhaps shifting customer expectations and preferences away from an existing segmentation model. Markel advises an annual “look under the hood” of the segmentation engine to see if segments are still valid or need appending/updating. An annual audit can avoid the expense of a broader overhaul down the road.

Big Data Blindness Ignores Potential Audiences

One outcome of segmentation based on existing customers is blindness to potential audiences. Segmentation research often uses the existing customer base and surveys of people that marketers assume should be targeted. This can create marketing campaigns that miss groups that Markel calls “ghost segments,” people who could be among a brand’s best prospective customers. Markel suggests a periodic look at non-customers for conversion potential as one way to capture these “ghosts.” And, of course, if a new product or service is in the works, research should ask whether it will attract new groups differing from the existing customer profile. Another reason ghost segments are common is that marketers, overwhelmed by the task of sifting “big data,” fall back on whatever data sets are handy. Markel suggests that it would be better to bring in big data at the tail end of segmentation. He advises analysts to start by creating segments using primary research, add existing customer “big data” to target those segments more efficiently, and then plug segments into a data management platform for insights on other products, services, interests, and media that may correlate.

Analytics Miss Without a Companywide Strategy

Finally, Markel stresses that a segmentation study that ends up residing only with a few marketing decision-makers will fail to live up to its ROI potential. Customer and prospect insights have relevance for multiple departments and teams, from sales to customer service to finance. In order to deliver a seamless, personalized customer experience, Markel suggests creating 360-degree customer personas and promoting them throughout the organization. Management can start with workshops to educate employees on the use and importance of those personas both for their departments and the organization, and then can schedule check-ins to show team members the resulting benefits of segmentation and targeting implementation. If segments are made relatable, it will ensure they are used and embraced across the organization.

Predictive Analytics Can Harness Data for Marketing ROI

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Beyond list brokerage, AccuList can support direct marketing clients with “predictive analytics,” meaning scientific analysis that leverages customer and donor data to predict future prospect and customer actions. It will scientifically “cherry-pick” names from overwhelming “big data” lists and other files. For example, AccuList’s experienced statisticians build customized Good Customer Match Models and Mail Match Models to optimize direct mail results for prospect lists, as well as one-on-one models for list owners to help acquire more new customers or donors. Plus, predictive models aid other marketing goals, such as retention, relationship management, reactivation, cross-sell, upsell and content marketing. Below are some key ways predictive analytics will harness data for better marketing ROI.

More Swift, Efficient and Effective Lead Scoring

Lead scoring is too often a sales and marketing collaboration, in which salespeople provide marketers with their criteria for a “good” lead and marketers score incoming responses, either automatically or manually, for contact or further nurturing. Predictive analytics will remove anecdotal/gut evaluation in favor of more accurate scoring based on data such as demographics/firmographics, actual behavior and sales value. It also speeds the scoring process, especially when combined with automation, so that “hot” leads get more immediate contact. And it allows for segmentation of scored leads so that they can be put on custom nurturing tracks more likely to promote conversion and sales.

Better List Segmentation for Prospecting, Retention and Messaging

With predictive analytics, list records can be segmented to achieve multiple goals. The most likely to respond can be prioritized in a direct mail campaign to increase cost-efficiency. Even more helpful for campaign ROI, predictive analytics can look at the lifetime value of current customers or donors and develop prospect matching so mailings capture higher-value new customers. Predictive analytics also can tailor content marketing and creative by analyzing which messages and images resonate with which customer segments, identified by demographics and behavior, in order to send the right creative to the right audience. Finally, analytics can develop house file segmentation for retention and reduced churn, looking at lapsed customers or donors to identify the data profiles, timing inflection points and warning signs that trigger outreach and nurturing campaigns.

Optimizing for Channel and Product/Services Offer

Data analysis and modeling can also be used to improve future marketing ROI in terms of channel preferences and even product/services development. By studying customer or donor response and behavior after acquisition, analytics can identify the most appropriate promotion and response channels, communication types, and preferred contact timing by target audience. Plus, a customer model can match demographics, psychographics and behavior with product and offer choices to tailor prospecting, as well as upsell or cross-sell opportunities, to boost future results.

Committing to a Good, Clean Customer Database

Reliable predictions require a database of clean, updated existing customer or donor records, with enough necessary demographics/firmographcs and transactional behavior for modeling. So, to prevent garbage-in-garbage-out results, AccuList also supports clients with list hygiene and management, including hygiene matching for DO NOT MAIL, NCOA and more, data appending of variables from outside lists, merge-purge eliminating duplicates and faulty records, response tracking with match-back, and more advanced list screening options.

Why Participate in Modeled Cooperative Databases?

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Today’s modeled cooperative databases offer big advantages for B2C and B2B direct marketers, which is why AccuList now represents 18 private modeled cooperative databases that clients can use to optimize direct mail results. These databases include millions of merged, deduped, and “modeled and scored” hotline names from thousands of commercial and nonprofit participants.  At no charge, each can match the client’s database, model client postal addresses, and deliver optimized “look-alike” names.  The database will prioritize those modeled names by decile or quintile to help clients further identify targets most likely to respond to an offer or fundraising appeal.

Fear of Sharing Misses Optimizing Opportunities

Marketers sometimes hesitate to participate because of unfounded fears of sharing exclusive/unique customers, catalog buyers, subscribers or donors with membership-based database participants. Note that these databases generally match a marketer’s names against the cooperative database files and share transactional data. If there are matches, only transactional information is added to the cooperative database records; and if there are no matches, the unique names are not added to the pool.  Why do cooperative databases opt to incorporate only multi-occurring or duplicate records? Because that is data that tends to be far more predictive, with proven response. Plus, the reality is that very few names are unique to a firm, publication or fundraiser. About 80% to 90% of consumer prospects are multi-buyers and so are in the database already, and 90% of nonprofit donors give to two or more organizations and so also are already included in cooperative data. On the other hand, by participating to access a huge pool of names rich with demographic and transactional information, marketers can tap many more optimized prospects, improve list segmentation and testing, bump up response and conversion, hone creative and offer targeting, and increase mailing efficiency.

Modeled Data Offers Cost-Effective Prospect and House Mailing

Acquisition campaigns clearly can benefit from netting look-alike prospects from the large cooperative database pool, a real boon for regional or niche mailers who struggle to find acquisition volume. The large universe also allows for more segmentation to target not only higher response groups but more valuable response segments. In the case of nonprofits, that could be high-dollar donors, for example. Profiling and modeling can create better results from house names, too. Instead of mailing the whole house file, current customers, subscribers or donors can be flagged for likelihood of response and upsell, for channel and messaging preference, for risk of lapse/attrition, and more. Plus, modeled databases offer cost efficiency via an attractive list CPM; recent, clean, deduped records that lower mailing costs; and optimization selects (or deselects) that also boost mailing efficiency and ROI. Check out these arguments for nonprofit participation in modeled cooperative databases, as well as these useful best-practices tips for commercial mailers from Chief Marketer and Target Marketing magazine posts.

Choosing One (or More) Modeled Cooperative Databases

As an industry-recognized list brokerage, AccuList now represents a long list of private modeled cooperative databases, some specializing in B2C, some in B2B, and many offering modeled names for both B2B and B2C campaigns. In addition, as a value-added option, some modeled cooperative databases feature omnichannel targeting services that allow matching of optimized direct mail names with digital media, including Facebook. We can help you choose the right solution to fit your marketing goals with the following leading cooperative databases:

  • Abacus Alliance
  • Alliant
  • American List Exchange (ALEXA)
  • Apogee
  • Dataline
  • DonorBase® (Founding Member)
  • Enertex
  • I-Behavior
  • MeritBase B2B Cooperative Database
  • OmniChannelBASE®
  • PATH2RESPONSE
  • Pinnacle Business Buyer Database
  • Pinnacle Prospect Plus
  • Prefer Network
  • Prospector Consumer Fundraising Database
  • Target Analytics
  • TRG Arts
  • Wiland

AI, Data, ‘Talent Culture’ Boost Incentive & Recognition Impacts

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AccuList’s many incentive and recognition products marketing clients should take a look at The Incentive Research Foundation’s “IRF 2019 Trends Study” for tips on where the market is headed this year.

Room for Growth With a Corporate Culture Stress

With economic growth and optimism strong, companies are continuing investment in incentive and recognition rewards, with considerable room for market expansion for product suppliers: 84% of businesses are now using non-cash rewards, but past studies show close to 60% of merchandise and gift card rewards are still sourced through retail versus specialized agencies or providers. One factor pushing the recognition market is the trend to “talent culture” creation by C-suite executives, with “The Incentive Marketplace Estimate Research Study” finding more employers than ever offering non-cash rewards aimed directly at building relationships, encouraging inclusion and knowledge-sharing, and promoting engagement. Why? IRF’s studies as well as academic research are finding that when executives combine economic incentives with recognition and well-designed non-cash rewards, they promote “corporate citizenship” behaviors and work environments that attract and retain top talent.

Continued Spending for Merchandise and Gift Cards

Overall use of merchandise rewards is expected to increase, per IRF, particularly among corporate audiences, with a net increase of 33% compared to a net 20% of suppliers and third-party providers. The use of logo’d brand-name merchandise dominates, with 75% of corporate programs using these items as rewards. Other popular rewards are electronics (63%) and clothing/apparel (59%). The average merchandise reward value is pegged at $160, pushed up by the small part of the market that spends more per reward; in fact, nearly a quarter of respondents indicate their average merchandise reward is $100, and half of respondents reporting average merchandise reward values falling between $1 and $100.  Meanwhile, gift cards continue to be a popular option within reward and recognition programs, with open loop cards (that can be used anywhere) and brand-specific cards both enjoying high utilization. Plus, e-gift cards are gaining momentum, with half of large enterprises and 58% of medium enterprises using them in 2018.

Analytics and AI Are Changing the Landscape

Of particular note, IRF’s most recent study urges reward program designers and suppliers to understand how predictive analytics and AI are changing the market: “In the incentives field, predictive analytics and machine learning are helping program designers understand who is drawn to which types of rewards, and how those rewards should be shaped and presented to produce the best outcomes on an individual basis. Organizations are using analytics and AI to see patterns in peer-to-peer recognition so they can encourage greater participation. Some are using it to personalize learning. In the near future, algorithms will spot patterns and correlations between past rewards and incentives and the desired behaviors and outcomes that define a high performer.” Read the full IRF trends study for more, including data on incentive travel and event gifting.

Fundraising Challenges Include Gen Z, E-mail, AI

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For AccuList USA’s nonprofit fundraising clients and fundraising consultants, 2019 will be another challenging year. Successful direct marketers will need to adapt to changes in demographics, technology and donor targeting, to name just a few trends recently cited by the Donorbox Nonprofit Blog.

Move Over Millennials; Here Comes Gen Z

Donorbox is sounding the alert ahead of the next demographic wave. While the Millennial generation is still the biggest cohort in the workforce, Gen Z is arriving. Born after 1996, they now make up an estimated 27% of the population and will account for 40% of all consumers by 2020. How are they different? The “2017 Global Trends in Giving Report” found that Gen Z members are interested in giving to many different causes, especially those involving youth, animals and human services. But to win the attention of these digital natives, messaging must be concise and engaging, offering an immediate experience that cuts through the marketing noise they routinely filter out. Gen Z is also the first mobile-only generation, so website, e-mail and donation forms must all be optimized for mobile. Plus, Gen Z likes visual-based platforms, so fundraising creative should use photos, videos and infographics to tell stories that grab attention.

Donors Expect Hyperpersonalized, Targeted Messaging

Accustomed to sophisticated digital technology that tailors messaging a la Amazon and Netflix, today’s donors expect a personalized, targeted approach that takes into account demographics, giving history and even psychographics. A generic appeal will fall flat. That means segmenting donor and prospect lists and using variable data printing to specialize messaging to account for generational differences and other demographics. It means tailoring the “ask” to the prospective donor’s income and giving history. It means refining giving/donation pages to highlight projects and wording that will resonate with the target donor group.

Donors Embrace E-mail Fundraising If Done Well

E-mail has gotten a bad rap recently because of crowded mailboxes, spam filtering and low response rates, but there is a lot to be said for revisiting e-mail strategy in 2019. For one, research shows that donors willing to donate through e-mail rose from just 6% in 2012 to 28% in 2018. Second, low-cost e-mail has an ROI of 122%, much higher than direct mail, social media and paid search. Finally, a backlash against social media abuses, including among the mobile-first generation, is improving e-mail’s digital appeal. But e-mail needs to be done well to deliver donors. Personalization and targeted messaging is expected, so, again, segment the audience by demographics, desired communication frequency, giving status, etc. Make sure there is a clear call to action, a compelling subject line, simple attractive visual design, and, most of all, impactful storytelling.

AI Can Help Turn Data Into Dollars

Artificial Intelligence (AI) is on its way to becoming ubiquitous in our society, and that will include fundraising. AI broadly refers to programs, computers and machines that perform “intelligent” tasks such as planning, learning, problem-solving, communication and more. AI can help nonprofits gather more data and use it better to advance missions and marketing. For example, one of the simplest uses of AI is a chatbot that interacts via messaging services like Facebook Messenger, Slack, Telegram, etc. A nonprofit can create a chatbot to handle donations, register members and distribute information about programs and services. AI also can be used to personalize donor journeys with tailored, personal messages based on real-time donor behavior and timed to encourage contributions. Finally, AI can weaponize data for more cost-effective donor development and marketing. For example, a donor’s giving and volunteering history, event attendance, affiliations, relationships, and data from wealth screening tools can all be analyzed to predict a potential donor’s likelihood to give a major gift.

See the complete list of eight fundraising trends identified by Donorbox.



fundraising trends for success

Weaponize B2B Data for 2019 With These Tactics

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Targeted, clean data is a key deliverable of AccuList USA’s data services and list brokerage efforts for business-to-business marketing clients. And as those clients prepare their 2019 plans, we urge them to take basic steps to ready their data-driven marketing for maximum performance. A Martech Today post by Scott Vaughn sets the stage by recommending five essential data-oriented strategies for B2B.

Precisely Defined Audience Targets Using Clean Data

Good response and conversion depend on identifying and engaging the right audiences, meaning the right companies and the right decision-makers within those companies, Vaughn reminds. To target that right audience requires processes for capturing critical data about prospects, customers and their purchase journey with precision, he asserts, and recommends a strategy of starting with a smaller universe of accounts and roles to more precisely define best targets–and then testing and using advanced strategies, such as predictive marketing and intent-data modeling, to expand to more accounts and buyers. But that kind of data targeting only works if marketers are looking at quality data, so data hygiene is another necessity. When a recent DemandGen survey finds that more than 35% of the data in existing databases is unmarketable on average, avoiding wasted dollars means instituting a “get clean, stay clean” data-hygiene effort for 2019, Vaughn urges. The hygiene regimen should include regularly auditing of data-capture processes and sources, using filters before data can enter the database, and maintaining a cleansing process to eliminate records that are invalid, non-standardized, duplicate or non-compliant.

Permission-Based Trust and Speedy Follow-up

Because today’s buyers are leery of companies and brands that don’t treat their information with care and because stringent data-privacy laws are being deployed around the globe, B2B marketers must have a proactive permission-based marketing plan for their data, warns Vaughn That includes asking for opt-in everywhere and having very visible, clear explanations of how behavioral data, such as website cookies, is used. Meanwhile, prospects and customers have not only come to expect data privacy, they have become used to the rapid, real-time response of the digital market. Yet for many B2B campaigns, it takes two or three days to follow up on a lead or inquiry, or even seven or eight days just to get leads loaded into marketing automation or CRM software! Vaughn proposes a concerted effort to speed data handling by identifying areas where data can be routed faster and reaction time reduced and then initiating sales and marketing training on speedier handling at each stage of the customer journey. That’s why many executive teams now prioritize a measure of “pipeline velocity,” meaning the time from when an opportunity is created to when the deal is closed, to improve revenues.

Agreeing on Measurements That Matter

Accurate, targeted, speedy data processes don’t automatically result in ROI improvement, however–not if data analysis ends up focused on the wrong metrics. Vaughn reports that high-performing marketing teams use insights with these key ingredients: agreed-upon key performance indicators (KPIs); tools that can measure performance; and easy-to-use dashboards that can help all stakeholders (marketing, sales, execs, etc.) make smarter decisions. For his complete article, see https://martechtoday.com/5-essential-strategies-b2b-marketers-must-master-in-2019-228066

Industrial Marketers Bet More on 2018 Direct & Digital

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AccuList USA has a long track record of helping warehouse, industrial and back-office product marketers via data brokerage, predictive analytics and multi-channel direct marketing, and we’ve learned some important lessons along the way.

Industrial & Tech Marketing Budgets Expand in 2018

The good news is that many industrial marketers were inspired to expand investment in 2018. According to the “2018 Budget Trends in Industrial & Technology Marketing” report published by engineering.com, industrial marketing budgets in 2018 are expected to hit “the highest levels of growth (45%) and the lowest reported levels of shrinking budgets (4%), of any of the last five years.” More than half (54%) of manufacturing marketers expect their budget to be larger in 2018.

Quality, Targeted Data Is Key to B2B Direct Marketing

But expanded multi-channel spending still needs to be smart spending. As data brokers, we can’t overemphasize that successful B2B direct marketing–including direct mail, print catalogs and e-mail campaigns–starts with quality, targeted data. Marketers can boost response by using predictive analytics and buyer profiles to target–and then opt for the rental lists of active product inquirers/buyers that our proprietary list research finds to be top performers in each vertical. Targeting the right message to decision-makers in the buying process is also key; with product and industry factors affecting whether to select a chief engineer, purchasing manager, warehouse manager, human resources chief, or C-suite executive in mailing lists.

A Digital Strategy Is Now Essential for Leads and Sales

While direct mail continues its response leadership, there’s no denying that most B2B buyers are digital shoppers today. Research by Acquity Group finds 94% of B2B buyers say they conduct some form of online research before purchasing a business product, for example. Forrester Research has found that 59% of B2B buyers prefer not to interact with a sales rep, and 74% find buying from a website more convenient. That makes digital catalog sites into essential sales tools, giving customers the option to browse product, pricing, and inventory information in real-time and then self-serve. Of course, online traffic-building requires a good search engine optimization (SEO) strategy given that 73% of global traffic to B2B companies comes from search engine results. But most successful B2B marketers also invest in paid digital efforts. In fact, a 2015 study by Content Marketing Institute, MarketingProfs, and Fathom found that manufacturers ranked search engine marketing highest among paid marketing options in terms of efficacy (52%) and promoted social media posts came in second (39%).  For social media ads, B2B marketers see video as a top response tactic, which is why manufacturers in the study ranked YouTube as the most effective social media site, followed by LinkedIn ads, which AccuList USA supports. Take a deeper dive into the core elements of digital industrial marketing with this post by gorilla76, a B2B consulting firm.