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Winning Sales with Graph Analytics: Uncovering Hidden Opportunities

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In an era where consumers are consistently inundated with sales pitches and generic advertisements, their tolerance for run-of-the-mill marketing grows thin. Today's consumer desires highly relevant, hyper-personalized communications tailored precisely to their unique experiences and preferences. In this dynamic business environment, grasping the nuanced intricacies of customer behaviour, relationships, and preferences has become paramount.

Enter graph analytics – a powerful tool that can help businesses tap into this trend, uncovering hidden opportunities by mapping and analysing complex relationships in their data. In this article, we explore how graph analytics can supercharge sales efforts, focusing on five key use cases.

What is Graph Analytics?

Before delving into the use cases, it's essential to grasp what graph analytics is. At its core, graph analytics involves studying the relationships between various data points. Unlike traditional data analytics that views data in rows and columns, graph analytics arrange it as nodes (entities) and edges (relationships), just like neural nodes connections in our brains. Coupled with graph data science algorithms, it enables businesses to uncover intricate patterns and connections otherwise not visible.

Top 5 Use Cases for Winning Sales with Graph Analytics

(1) Customer Journey Mapping

Understanding the customer's journey is more than just tracking touchpoints; it's about unravelling the intricate web of interactions, decisions, and influences that guide a customer's path. Graph analytics delves deep into this web of interactions, identifying not just where customers engage or disengage, but understanding the multifaceted relationships and influences that determine these decisions. Such insights empower businesses to enhance the efficacy of their sales funnels and boost conversion rates by addressing specific nuances in the journey.

An example of a bank’s new customer:

Consider a prospective customer, Yuna. Yuna recently graduated and secured her first job. As she starts managing her finances, she realizes that her current student account no longer suits her evolving financial needs. One day, while catching up on her social media feed, she encounters a targeted ad from a bank, promoting its 'Young Professional Banking Package.' The ad piques her curiosity, prompting her to click and explore the bank's dedicated landing page. Though she goes over the benefits, she doesn't commit immediately.

A few days later, over a casual coffee chat, one of her peers, Leo, speaks highly of the same bank, mentioning that he's been enjoying various perks and benefits tailored for young professionals like them. He shares a "referral code" with Yuna, explaining that by using this code, both of them would receive incentives from the bank upon her sign-up.

The next week, while seeking advice on financial planning, Yuna stumbles upon an article on a renowned blog. Within the article, she finds a link directing her to the bank's financial literacy webinar series, particularly curated for young professionals. Intrigued once again, she registers for it. Post-webinar, the bank sends Yuna an appreciative email for her participation and offers her a complimentary one-month financial advisory session if she proceeds to open an account.

Taking into account the bank's consistent digital outreach, Leo's personal recommendation, and the allure of shared incentives through the recommendation code, Yuna finally decides to open an account with the bank.

While traditional analytics might simply trace Yuna's journey through her digital interactions—the ad click, the webinar participation, and the eventual account initiation—graph analytics offers a far more textured narrative. It underscores the interconnected influences, highlighting how Leo's recommendation, complemented by the bank's personalized outreach and incentives, collectively led to Yuna's conversion.

Such granular insights empower the bank to fine-tune its digital and referral strategies, identifying the most impactful channels fostering trust and conversion, thereby ensuring more targeted marketing investments and co-ordination among the various channels.

(2) Customer 360 View

Creating a comprehensive view of a customer is akin to assembling a jigsaw puzzle, where each piece represents a facet of the customer's interaction, preference, or history with a brand. Graph analytics acts as a master assembler, correlating data from diverse sources like CRM systems, social media, and purchase histories. By interpreting these multifaceted relationships, businesses can derive a more holistic and interconnected understanding of each customer. Such a consolidated perspective is pivotal for refining marketing strategies, addressing pain points, and crafting effective upselling or cross-selling opportunities.

An example of a bank’s customer 360:

Imagine a long-time customer of a bank, named Jamal. Jamal has several touchpoints and interactions with the bank over the years:

  • He holds a savings account which he opened a decade ago.

  • He took out a home loan five years back and has been making regular monthly repayments.

  • Jamal uses the bank's mobile app regularly to check balances, pay bills, and transfer money.

  • He occasionally interacts with customer service, mostly via the bank's chatbot, but sometimes via direct calls.

  • Jamal follows the bank on social media and sometimes engages with their posts.

  • He recently browsed the bank's section on investment portfolios but didn't initiate any service.

A traditional view might treat each of these as isolated interactions. However, using graph analytics, the bank pieces together a holistic view of Jamal.

From Jamal's savings account data, the bank understands his monthly income and spending patterns. The home loan data reveals a consistent repayment history, indicating financial reliability. His mobile app usage, especially frequent money transfers to an account with a "university" label, might suggest he's supporting a family member's education. Customer service interactions reveal he's tech-savvy but occasionally needs assistance with new features. His social media engagements and recent browsing history hint at a growing interest in investments.

Using this interconnected data, the bank sees Jamal not just as a customer of discrete products but as a financially responsible individual, potentially supporting a child's education and showing a budding interest in investment options.

With this 360 view, the bank can now craft tailored strategies. Perhaps they offer Jamal a student loan package for his child or introduce him to a financial advisor who can guide him on investment choices suitable for his financial profile.

By integrating and analysing data points using graph analytics, the bank transcends fragmented customer understanding and can engage Jamal in a more meaningful and personalized manner.

(3) Hyper-Personalized Product Recommendations

In the realm of product recommendations, relevance is key. Through graph analytics, companies can uncover intricate relationships between products and discern patterns in customer behaviours. By examining the deeper connections, like shared behaviours or mutual preferences among customers, businesses can offer recommendations that resonate more profoundly with individual customer inclinations. Such refined recommendations not only enhance the user experience but can also lead to increased cart sizes and repeat transactions.

An example of skincare product customer:

Consider Zoe, a consumer who recently visited the website of "GlowSkin," a leading skincare manufacturer. Zoe is on a quest to find products that cater to her sensitive skin and reduce early signs of aging.

She starts by browsing the 'Sensitive Skin' section and spends considerable time reading about a particular hydrating serum. Then, she navigates to the 'Anti-Aging' collection and examines a few eye creams. During her browsing, she also completes a short skincare quiz indicating concerns about skin dryness, occasional breakouts, and sun exposure. On her profile, Zoe has previously marked products she loved, which includes a night cream from the 'Sensitive Skin' range.

Traditionally, based on her recent interactions, she might get a straightforward recommendation for the serum or the eye cream. But with graph analytics, "GlowSkin" can create a more nuanced recommendation.

Analyzing patterns across their customer base, "GlowSkin" might find that many customers who purchased the hydrating serum for sensitive skin often bought a particular sunscreen that's gentle yet effective against premature aging. Moreover, they might find that users who liked the night cream Zoe loved also preferred an anti-aging mask, showing excellent results for sensitive skin.

Using these intricate relationships, "GlowSkin" doesn't just recommend the hydrating serum or the eye cream to Zoe. They suggest the synergistic sunscreen and the anti-aging mask, tailoring the recommendation to her unique skin concerns and preferences.

Upon seeing such hyper-personalized recommendations, Zoe feels understood. She's more inclined to trust "GlowSkin," leading her to purchase multiple products and becoming a repeat customer. Over time, the consistent and relevant product suggestions, rooted in the deep understanding that graph analytics provides, solidify her loyalty to the brand.

Hyper-personalized recommendation engines are powerful tools to be incorporated in chatbots, apps and other sales channels.

(4) Hyper-Targeted Customer Segmentation/Marketing

Beyond the rudimentary categorizations like age or location, today's customer segmentation demands a deeper dive into shared behaviours, interconnected preferences, and mutual interactions. Graph analytics shines in this domain, sifting through vast datasets to identify nuanced clusters of customers bound by shared characteristics or behaviours.

These hyper-targeted segments, when approached with tailored marketing campaigns, can drastically elevate engagement and conversion rates, ensuring marketing efforts resonate with the right audience in the right manner.

An example of an automotive manufacturer:

Consider "AutoElite," a renowned car manufacturer known for its diverse range of vehicles, from compact cars to luxury SUVs. With the launch of their new hybrid series, they want to maximize reach and engagement without oversaturating the market with generic advertisements.

Historically, AutoElite classified potential customers based on age, income, and perhaps past purchase history. However, with the rising importance of environmental consciousness, urban living dynamics, and changing work-from-home trends, they realized the need for a more sophisticated segmentation strategy.

Enter graph analytics. Using this approach, AutoElite identifies:

  • Eco-Enthusiasts: Customers who have shown interest in or have previously purchased environmentally-friendly vehicles, and also participated in green initiatives or events sponsored by the company.

  • Urban Navigators: Individuals living in dense city areas where parking is a premium, and there's a growing trend of seeking fuel-efficient, compact, yet powerful vehicles.

  • Versatile Commuters: Customers who switch between city driving during the week and countryside getaways during weekends, indicating a need for a vehicle that balances fuel efficiency with performance.

  • Tech Trendsetters: Those who show keen interest in the latest car technologies, perhaps attending tech-focused car showcases or engaging with the brand during tech expos.

Equipped with these hyper-targeted segments, AutoElite crafts distinct marketing campaigns:

  • For Eco-Enthusiasts, they emphasize the low carbon footprint and innovative green technologies in the hybrid series.

  • Urban Navigators receive campaigns showcasing the compact design, ease of parking, and the cost savings from fewer fuel stops.

  • Marketing for Versatile Commuters underscores the car's dual-nature: efficient for the work week, powerful for the weekend adventures.

  • And Tech Trendsetters are introduced to the cutting-edge tech features, infotainment system upgrades, and smart integrations in the new hybrid range.

The result? Instead of a broad-brushed approach, AutoElite's campaigns resonate deeply with the specific needs and aspirations of each segment, leading to better engagement, more test drives, and higher sales conversions. This precision in marketing ensures that potential buyers feel understood and valued, driving both brand loyalty and business growth.

(5) Channel Attributions

Decoding the effectiveness of marketing channels isn't just about tracking conversions. It's about understanding the labyrinthine interactions and influences leading up to that conversion. Graph analytics meticulously examines this intricate web, attributing sales to specific channels with a higher degree of accuracy. Such granularity in understanding allows businesses to allocate resources and budgets more intelligently, directing investments towards channels that genuinely drive ROI.

An example of an insurer:

"InsureMax" is a major insurance company with a vast array of products, from life and health insurance to auto and home coverage. They primarily rely on their vast network of independent agents to reach potential customers, alongside other channels like online ads, email campaigns, informational webinars, and more.

Here's the multi-channel journey of Jason, a middle-aged professional, leading to his purchase of a comprehensive life insurance policy:

  • Digital Discovery: Jason's first interaction with "InsureMax" was through a targeted online ad, while he was reading a financial blog. The ad highlighted the benefits of having life insurance, prompting him to click and browse InsureMax's website. Though he found the information helpful, he wasn't ready to commit.

  • Email Campaign: A week later, Jason received an email from "InsureMax" (thanks to his online registration on the website) inviting him to a webinar explaining the intricacies of life insurance and its long-term benefits. Intrigued, Jason attended the webinar to learn more.

  • Agent Anna's Proactive Approach: Around this time, Agent Anna, an independent agent affiliated with "InsureMax", got Jason's contact through the company's lead generation system which indicated his recent interactions with the brand. Anna reached out, offering to answer any questions Jason might have post-webinar. Their phone conversation was insightful, with Anna addressing Jason's concerns and explaining the policy's benefits tailored to his needs.

  • Personalized Follow-up: Post their phone conversation, Anna sent Jason a personalized package – a combination of digital resources and brochures – detailing the policy options suitable for him.

  • Decision and Purchase: After mulling over for a week and another clarifying call with Anna, Jason decided to purchase the life insurance policy, recognizing its alignment with his long-term financial goals.

While a cursory glance would attribute Jason's purchase primarily to his interactions with Agent Anna, graph analytics underscores the value of each touchpoint. It highlights how the initial online ad and subsequent webinar played crucial roles in warming Jason up for Anna's outreach. These insights allow "InsureMax" to optimize its multi-channel marketing efforts, ensuring agents like Anna receive warm leads more likely to convert.

With graph, businesses can assess the efficacy of each channel in a more holistic way and attribute the sales in a way that helps harness the collaborative effort of these channels.

Final Thoughts

Graph analytics has revolutionized the way businesses approach sales and marketing strategies. By unveiling hidden patterns and relationships in vast amounts of data, it offers opportunities to engage customers in unprecedented ways. Whether it's refining the customer journey, providing razor-sharp product recommendations, or optimizing marketing channels, graph analytics is a powerful ally for businesses looking to stay ahead in the sales game.


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