The Importance of Customer Behavior for eCommerce Marketing User behavior is the key force for developing effective marketing strategies, and fully integrated B2B platforms can deliver many of these insights automatically. Brick-and-mortar companies can ask each customer for information, but website visitors won't reveal information until they're ready to buy. However, website behavior, customer actions in other channels, buying histories, eCommerce business intelligence reports, and time spent on various online channels, website features, and related social media pages can deliver an incredible body of data. B2B websites run on data, and user metrics can provide information about how to market to customers and prospects when to approach them, and which products to recommend. Reaching people at optimal times in the buying cycle with the right support materials can increase conversion rates with predictive analytics and targeted marketing. Business intelligence for eCommerce can provide a more complete picture of customer behavior because about 55 percent of people research products on social media, and 80 percent of B2B decision-makers prefer getting their information from articles and other kinds of content instead of traditional ads.[1] Tracking buyer behavior across channels can generate some valuable data that companies can use to target advertising, generate leads, increase conversions, and fine-tune their content. McKinsey reports that B2B customers use an average of six channels during their decision-making process, and they conduct this research on different computing devices while consulting with sales teams, texting, interacting with customer service centers, and commuting.[2] All these behavioral actions provide a wealth of behavioral data that companies can mine and use to their advantage. Segregating Customers Based on User Behavior There are many ways to segregate B2B customers, and these include onsite behavior, social media activity, ordering history, company size and resources, visitor engagement levels, website navigation habits, and analytical techniques like A/B/multivariate testing. A few of these segmentation possibilities include: Customer Segmentation This approach segregates customers based on each user's behavior and helps to provide a personalized Web experience and segregates prospects and new visitors based on similar characteristics. Purchase Path Analyses Using this method, marketers gain insights based on key products and the additional products that buyers often purchase in with them. These insights can be further refined based on seasonal sales, special promotions, or regular sales. Segmentation data provide expanded opportunities for cross-selling, upselling, and targeted marketing. Basket Segmentation It's not always possible to gain solid customer profiles--especially for new visitors--but basket segmentation can close the information gap by making assumptions based on the products purchased. Predictive Modeling This method of using behavioral data helps to target users based on what customers with similar profiles do. Predictive capabilities include forecasting a product's appeal to a given customer, predicting ROI for marketing efforts, and fine-tuning marketing messages. There are also many ways to classify customers, and these can certainly vary from industry to industry, but some of the most common include: The bargain shopper, which is self-explanatory Buyers who take a surgical approach with lots of keyword filtering Enthusiast buyers who buy based on certain characteristics such as adventuresome products, better workmanship, eco-friendly manufacturing, and other benchmarks Power shoppers who buy based on reviews, popularity, trendiness, manufacturer reputation, and similar characteristics Committee shoppers who find products that satisfy complex requirements or provide strong B2B support feature that will help to convince multiple decision-makers Impulse buyers who buy based on real-time impulses, incentives and other promotional strategies that encourage impulse buys Using Google Analytics to Refine Customer Insights Google Analytics can help marketers understand customer behavior. Google regularly churns out new techniques that fall into the reporting categories of Behavioral Flow Reporting, User ID, Data Import, Enhanced eCommerce, and Audience Reporting. Behavioral Flow Reporting Flow reports chronicle user journeys to reveal how customers navigate on the site, which shows correlated data about which site areas are the most popular and which areas don't receive much traffic. Analysts can segregate statistics by geographical region, product campaigns, traffic source, and new reporting benchmarks such as what products are viewed most often, which areas generate conversions, and other advanced metrics. User ID User ID analytics delivers powerful insights such as age, gender, number of transactions, lifetime customer value, and type of device the customer typically uses. Analysts can get a complete picture of the client by aggregating ID information when users access the site from multiple devices, which often resulted in creating separate customer IDs and profiles in the past. Google's Universal Analytics brings together all this separate ID information, so analysts get a detailed picture of each user’s behavior across devices. Data Import Data import allows companies to import data about customer buying trends, browsing habits, and what users do after leaving a website. However, using this data to identify particular customers is against Google policies, so the feature is only useful for determining statistical trends. Enhanced eCommerce This Google plugin is custom-designed for eCommerce applications and measures product impressions, website clicks, how often products are added to shopping carts, cart abandonment rates, sales conversions, refunds, and returns. Other valuable metrics include aggregate shipping costs, taxes, customs and duties, and other actionable financial data. Audience Reporting Google's Audience Reporting delivers key audience information that includes the number of users over various intervals, pageviews, bounce rates, pages visited per session, and percentage of new users. Demographic overviews break down visitors by key demographic categories. This information proves invaluable when planning marketing initiatives for key groups of customers. Leveraging Business Intelligence to Build Expanded Behavioral Profiles Business intelligence becomes increasingly essential in nurturing B2B relationships, getting order approvals from multiple decision-makers, and building a 360-degree profile of new and existing customers. Automatic platform integrations between BI and CRM systems can deliver more precise demographic data, connect with supply chain systems, uncover customer behavior on other websites, and build profiles based on user likes, followers, blog posts, reviews, and social media activities. Armed with a fully integrated report, companies can predict which sales strategies are most likely to result in conversions, and BI data give companies a strong competitive advantage over business rivals. Integrated platforms offer strong custom tools to manage information through custom dashboards. This feature not only provides visual patterns and consolidates BI and internal behavioral insights but also tracks and analyzes customer behavior in real-time so that sales staff can intervene at optimal times to offer incentives, answer questions, and close deals. The right customizations aggregate BI and internal information--such as business reporting, past ordering histories, social likes and dislikes, and profiles of company decision-makers--and transform the raw data into actionable promotional strategies. Applying Data Mining to Predict Buyer Behavior Data mining at its simplest detects patterns in a given database. The database can be internal user metrics, public information, BI data, social media-related, industry-generated or databases of business associates, and third-party website integrations. Data mining can be used to identify customer churn rates, detect fraud, identify common customer behaviors, segregate customers for marketing messages, and analyze buying behavior and economic trends. Key B2B platform integrations make it possible to automate predictive data such as which customers to target with direct mail and which clients respond better to emails or mobile marketing texts. Other key data mining insights include pinpointing which products are usually bought together, discovering errors caused by miskeyed data, understanding B2B buying habits, and revealing many other predictive insights such as seasonal buying habits, spending during economic downturns, and purchasing habits when the economy is booming. Best Practices for Using Buyer Behavior for Marketing Strategies Inbound marketing delivers the highest conversion rates at the lowest costs, so tapping this resource to its fullest potential should be your goal. User metrics deliver astonishing insights that can be used in your marketing strategies, but you need a platform that's fully integrated and capable of generating automatic marketing responses based on buyer behavior. Integrating these capabilities requires some customization and development, but the results fully justify development costs. Choosing a collaborative partner can speed development, simplify the process and involve your IT team in a collaborative arrangement where each team member becomes proficient in the technologies and their eCommerce applications. References: [1] blog.behalf.com: 16 B2B Stats To Help Your 2016 Marketing Strategy blog.behalf.com/16-b2b-stats-to-help-your-marketing-strategy-in-2016/ [2] McKinsey.com: Do you really understand how your business customers buy? www.mckinsey.com/business-functions/marketing-and-sales/our-insights/do-you-really-understand-how-your-business-customers-buy