Customer-facing marketing material usually apprises a prospect about a company’s products or services and their benefits. Persuasive content can lead to conversion while feeble content can steer a prospect toward competitors. This makes marketing collaterals important elements of an organization’s success. Key parameters for evaluating the maturity of customer-facing content are:
- Understand the target audience – Specificity in language is achieved by keeping the ideal buyer in mind. For example, “From Brand A’s Honeydew collection, this shopping cart helps children advance their social skills through inventive play.” is a description of a toy shopping cart and is focused on children.
- What’s in it for me? It is important to focus on the performance aspect of products or services, highlighting benefits. For example, “This Thermal Razor by Brand B provides heat instantly at the push of a button, giving you a comfortable shave.” is a description of a razor. It clearly explains how the razor enables a comfortable shave.
- Back your words – Assertions should always be backed by evidence. This will also help stay away from legal hassles. For example, a mobile device provider’s website reads: “This device looks spectacular and comes in two of the most loved color variants: gold and black. Brand C always focuses on product quality and delivers the best after-sales service.” Though the website assures the “best” after-sales service, there’s no concrete proof supporting the claim, and such statements should be avoided.
- Use words that stimulate the senses – Enticing the senses by using the right adverbs and adjectives in marketing content often results in purchases. For example, the description “Choose from our frozen desserts featuring rich ganache, velvety caramel, luscious praline, refreshing fruit, and crunchy nuts.” makes use of multiple words to elevate the content. However, that doesn’t mean content should be saturated with adjectives and adverbs just to paint a colorful picture of the company’s products.
NLP-driven qualitative assessment can help organizations across verticals and horizontals comprehend the quality of their sales and marketing content published across various platforms. The output of the analysis can be used to offer valuable feedback to content writers and marketing professionals, who in turn can help enhance content quality and overall sales of the organization.
User-facing marketing content can be made customer-centric by targeting a set of people who are potential buyers of the product. For example, the sentence “Since ages, Indian mothers have been realizing that Brand C Drink helps choosy toddlers get the required nutrition to grow taller and gain weight.” has been used to attract parents and children. Another example would be: “Our fully breathable mattress with feather-soft material keeps parents happy and children comfortable.” to attract parents.
The words “mothers”, “toddlers”, “parents” and “children” are common nouns and represent groups of people. “Mothers” is used as the subject of the sentence. The algorithm to identify customer-centricity should be aimed at determining these unique features.
User-facing content such as marketing content in product descriptions, brochures, flyers and websites apprises prospects about the organization’s products, services and their benefits. A compelling, customer-centric copy with an appropriate focus on: benefits, solid claims or assertions backed with proof, and words that stimulate the senses can result in increased conversions. At the same time, content that doesn’t include any of these essential features can shift the prospects’ attention toward competitors.
Marketing professionals, creative copywriters and sales executives can leverage the automated mechanism detailed in this whitepaper to assess the quality of their marketing content by scoring the usage of personal, sensorial, functional and superlative words and phrases in their material. The method proves that lexical, syntactic and contextual NLP techniques can help improve sales by enhancing the quality of content.
To know more read our whitepaper here
About the author:
Siddharth is a Lead Data Scientist at Happiest Minds. His role primarily involves solving problems in the NLP, Optical Character Reader (OCR) and Chatbot areas. He is also responsible for helping customers address challenges in the Data Analytics area. His love for his job is driven by his interest in writing code and playing with data structures, and he likes to share his knowledge with the wider community. Siddharth has worked across multiple domains such as Digital Marketing, Edu-tech and Construction.