Since video is an indispensable component of modern marketing strategies, many companies run video campaigns across multiple platforms, from Instagram to traditionally static platforms like email. In practice, marketers often create separate videos for each client in a small audience, such as B2B clients. Often dynamically served within video templates are small chunks of personalized video content, a vital component of lead-nurturing programs that boosts retention and that cuts through the noise of advertisements. If correctly implemented, video personalization is an effective way in which to connect and build trust with customers. <\/p>\n\n\n\n
Nonetheless, the use of personal data is a sensitive subject because advertising has become more complex in recent years due to regulations and increased alertness of privacy issues. Still, although customers are less impressed by trivial customizations like their names in a video title, statistics show that customers are engaging with personalized content more and more.<\/p>\n\n\n\n
Over-personalized video content that\u2019s customized with too many customer-specific data dimensions is unsettling, however. The challenge lies in finding the sweet spot to maximize engagement<\/a>.\u00a0<\/p>\n\n\n\n This article suggests ways in which to determine the proper amount of video personalization to engage, not repel, customers.<\/p>\n\n\n\n Gartner<\/a> found that customers are at least three times more likely to give up on brands that over-personalize than those that under-personalize. So, keep in mind these goals for content personalization:<\/p>\n\n\n\n Follow the rules below to accelerate testing and quickly find the right amount of data for personalizing content.<\/p>\n\n\n\n Most video advertising leaves an impression within the first three to <\/a>five seconds. On mobile-first platforms like Instagram, you might have even less time as people scroll through their feed. Hence the tendency to start a video with a personalized banner or other attention-grabbing, specific data right away.<\/p>\n\n\n\n However, customers often balk at such blatant tailoring of content. As a rule, broad, personalized lead-ins like the geographical area or industry are acceptable. Anything more specific at the start of the video can seem creepy.<\/p>\n\n\n\n Instead, first identify the type of useful information your customer desires. For example, if a sports-equipment company\u2019s repeat customers regularly make apparel purchases, set up a workflow that assembles two or three short clips of broadly defined apparel and send them to the repeat customers by email. Feature a different shoppable product<\/a> in each of the clips but not the merchandise the customer has already purchased. Also, never explicitly point out that \u201cyou know\u201d exactly what the customer is buying. That\u2019s both unhelpful and disturbing.<\/p>\n\n\n\n In general, the common data types\u2014or data dimensions\u2014in your personalized content are the identities of your customers, their locations, their past actions, and the actions they will likely take. Research<\/a> suggests that customers find over three data dimensions vexing and off-putting. Ideally, offer customers an entry into a personalized experience with helpful recommendations but leave room for exploration and customization.<\/p>\n\n\n\n For example, you could combine age and gender information with geo-fenced data to serve tickets for a local event. You could also add a third dimension by proposing a path for the customer\u2019s next steps, such as food purchases, in a mobile-friendly format.<\/p>\n\n\n\n Be sure not to over-personalize. It might be appropriate to recommend restaurants for lunch based on the time of day, but suggesting a local diner that the customer has visited before involves a fourth data dimension. Even though precise, a recommendation like that might be too coercive.<\/p>\n\n\n\n Most customers would agree to use of their data if they believe that you respect that privilege. So, be alert to the boundaries to avoid causing your customers to feel that they\u2019re being guided to a foregone conclusion. Also, use as little customer data as necessary to make it less obvious\u2014even when customers know that you\u2019re using their information.<\/p>\n\n\n\n To navigate all that, integrate personalization at a high level\u2014even higher than the focus of many strategies\u2014by targeting personas. If a video message primarily builds on your company\u2019s story<\/a> with customer data serving as an accessory, even a single data point would be an effective personalized video for B2B campaigns. Such an approach is even more crucial and complex in a B2B context because customer engagement is higher for personalized video content in B2B than in B2C. In contrast, since it caters to individual interests, individualized personalization actually tends to s<\/a>low the purchase process.<\/p>\n\n\n\n Take, for example, a multicloud, cost-management platform. To highlight the simplicity of managing deployments under a single framework, you could generate three videos for different positions in a customer\u2019s organization, each with a different emphasis: reduced workload and complexity (for developers), increased security and cost accountability (for IT leaders), and cost savings (for CFOs).<\/p>\n\n\n\n That way, you bury the data in a well-integrated personalization scheme. By subtly tailoring the message so that it mainly speaks to the needs of technical teams while also engaging with the individual who\u2019s watching the video, you ensure that the message is not intensely personal. <\/p>\n\n\n\n If placed at the beginning of a video, personalized content can make customers feel as if they were conversing with a stranger who knows more about them than appropriate. Instead, place customer data at the end, where personalization builds on an interaction that the customer has controlled from the start. Remember that you build trust by first offering customers an open door with plenty of space to explore, followed by a message that focuses on their more specific needs.<\/p>\n\n\n\n Again, easing customers into a personalized application of their data eliminates creepiness\u2014even if they know that you\u2019re using their personal data.<\/p>\n\n\n\n Doubtless, video has quickly become the most compelling media format for marketing whereas static video fails to generate new leads and to entice customers to keep returning. Hence the attractive promise of increased engagement through personalization.<\/p>\n\n\n\n On the flip side, video is susceptible to too much personalization. The directness of video, which makes it engaging and immediate, is offensive if the content is too specific to the viewer. So, keep these guidelines in mind:<\/p>\n\n\n\n With Cloudinary, which offers an effective and automated way in which to serve video content optimized for all devices and connections, you can quickly and easily personalize content with tools like AI-powered content-aware cropping<\/a>, shoppable video<\/a>, and auto-generated video slideshows<\/a>. The platform also captures critical data for analytics. For details, contact us<\/a> today!<\/p>\n","protected":false},"excerpt":{"rendered":" Since video is an indispensable component of modern marketing strategies, many companies run video campaigns across multiple platforms, from Instagram to traditionally static platforms like email. In practice, marketers often create separate videos for each client in a small audience, such as B2B clients. Often dynamically served within video templates are small chunks of personalized […]<\/p>\n","protected":false},"author":18,"featured_media":23951,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_cloudinary_featured_overwrite":false,"footnotes":""},"categories":[1],"tags":[25,270,303,305,310],"class_list":["post-23833","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","tag-asset-management","tag-shoppable-video","tag-video","tag-video-api","tag-video-player"],"acf":[],"yoast_head":"\nFinding the Personalization Balance<\/h2>\n\n\n\n
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Don\u2019t Start With Customer Data<\/h3>\n\n\n\n
Don\u2019t Use Too Many Dimensions<\/h3>\n\n\n\n
Do Use As Little Customer Data As Possible <\/h3>\n\n\n\n
Do Use Customer Data As Late As Possible<\/h3>\n\n\n\n
Establishing Balanced Personalization<\/h2>\n\n\n\n
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