Strategies Can Bolster Fundraising If the Economy Turns Rocky

Although economic growth and jobs data continue strong at the start of the year, many AccuList® nonprofit clients still express anxiety about the impact on charitable giving of higher inflation or even recession later in 2023. Here's the advice from fundraising veterans: Stay the course and avoid the temptation to cut back on direct mail or digital marketing. When fundraisers reduce the channels and frequency of donor requests, they cut opportunities for giving and erode long-term connections. 

A better way to offset economic drags is to increase the efficiency of donor acquisition and retention programs. For direct mailings, costs can be reduced by tactics such as modified package size and pre-sorting, as well as by greater mailing list efficiency. Using the best-performing vertical lists and modeled data can boost donor prospecting ROI, for example, while house donor lists can select out lower-value or lower-response segments. 

In tougher times, nonprofits also can't afford the waste of bad data. OneCause, a provider of event and online fundraising technology, found that only 18% of nonprofits reported having enough data and insights for cost-effective decision-making in 2022! So fundraisers should prioritize clean data—de-duped, complete, consistent, deliverable and actionable—for cost-effective targeting. 

Fundraisers also can take better advantage of the fact that today's donors are multichannel responders by coordinating direct mail with boosted social media outreach as well as online and mobile giving. In-person fundraising events are expected to make a comeback in 2023, too. OneCause reports 83% of organizations plan to hold at least one in-person event in 2023. Data supports that investment in event and online efforts: Over half of nonprofits surveyed reported generating 21% of operating budget from event and online fundraising in 2022. 

For help with donor acquisition, multichannel fundraising and data management, see https://www.acculist.com/fundraising/

Avoid These Segmentation Errors for Max List ROI

List segmentation is key in targeted direct marketing, and 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, identifies some of the common strategic errors. First, 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." 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. One outcome of segmentation based on existing customers or surveys of people marketers assume are the right targets is blindness to potential audiences that Markel calls "ghost segments." 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 segments more efficiently, and then plug segments into a data management platform for insights on other products, services, interests, and media that may correlate. Finally, Markel stresses that a segmentation study will fail to live up to its ROI potential unless it informs the whole organization. Customer and prospect insights have relevance for multiple departments and teams, from sales to customer service to finance. Markel suggests creating 360-degree customer personas and promoting them throughout the organization via workshops and periodic team updates on results. For more, see https://www.acculist.com/avoid-segmentation-missteps-to-boost-list-roi/

Why Use Modeled Cooperative Databases for Mail?

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. 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 90% of nonprofit donors give to two or more organizations, so these names 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 reap many gains. 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. For more, see https://www.acculist.com/why-participate-in-modeled-cooperative-databases/