Senior Customer Insight Analyst
London / £50000 - £60000
£50000 - £60000
SENIOR CUSTOMER INSIGHT ANALYST
We are working with a leading retailer looking for an experience marketing analyst to lead the teams marketing analytics roadmap and provide key insight to senior stakeholders.
As a Senior Customer Insight Analyst, you will be focussing upon looking at customer's engagement with products and how to develop a clear customer view to enhance decision making.
- Work with the 3 main company stakeholders.
- Work to understand the customer base to ensure engagement is kept at its optimum.
- Projects include: Customer Lifecycle Management, Segmentation, Profiling, Churn Prevention, Revenue Per User/CLV.
YOUR SKILLS AND EXPERIENCE:
The successful Senior Customer Insight Analyst will have the following skills and experience:
- Salary: up to £60,000
- WFH completely at the moment.
Is Product Analytics the new Digital Analytics? | Harnham Recruitment post
Following on from our exploration of what Digital Analytics is, and the exploration specifically of hiring Digital Insights Analysts in the North of England and Midlands, we wanted to take a look at Product Analytics, and how it differs from the standard Digital Analyst role.To help investigate the importance of Product Analytics in the current market, we have interviewed Nicky Tran, a Product Analyst at Virgin Media (Manchester).What Is A Product Analyst?In simple terms, a Product Analyst ‘’looks at the different products a company has, and then you are identifying which areas of the product can be improved or which areas can be optimised.” While Digital Analytics can inform the product lifecycle, the interesting aspect to this role is, that unlike a traditional Web Analyst role, it is more of a hybrid role. Nicky emphasised that it is ‘’an upcoming sector within the analytics community’’, providing an overlap between Digital Analytics, Customer Analytics and Data Science.The key skills and tools for this role are advanced SQL, Google Analytics, and AB testing. So how does this skillset differ from a traditional Web Analyst? Nicky suggests that while the core requirements are that of a Web Analyst, with a web role you would essentially just be using Google Analytics Data. However, as a Product Analyst, you would be using advanced SQL to access other data bases, and pull data from models, and therefore, “you are combining two sets of data to get a more insightful look”.Why Is Product Analytics Important, And Why Are They Now Becoming More Prominent On The Market?Similar to Digital Analytics roles, it is clear that with the impending digital transformation, companies are becoming increasingly data-led, especially with regards to their digital platforms (and products).As a result of the pandemic, the digital space is so much more important than ever before. Therefore, to stay competitive, and to really understand the products from the consumer perspective, companies have to provide the most personalised customer experiences to acquire and retain their consumers. As Nicky mentions, ‘It is definitely worth making an ‘inventory’ to see how to promote what you have – it is about personalising the customer journey’.What are employers looking for in a Product Analytics candidate?Product Analytics are great due to their hybridity. In the current market, where there are numerous jobs, and few candidates, a Product Analyst (technically strong in three areas) is a highly sought-after rarity.Businesses are becoming increasingly invested in Product Analytics and having a Product team that works alongside the Digital team can be beneficial; especially when companies need to stay competitive.What are Candidates looking for? Understanding the differences between a Digital Analyst, and a Product Analyst is key to understanding what a candidate is looking for. Nicky suggested that this Product Analyst role enabled her to be the ‘bridge’ between areas.So how does the future of a Product Analyst differ to that of the route of a Digital Analyst? For Nicky, this is one of the most important factors to being a Digital Analyst, as she has the option to go down the Data Science route in the future should she wish. The more technical skills she has as a Product Analyst means she is building up experience across different areas of Data & Analytics, giving her a slightly different career path, should she want to go down a more technical route.Why Choose A Product Analyst Role?“If you come from a technical background – maths, physics, computer science – and are interested in how the numbers are crunching, it is worth going into Product Analytics, as it needs a logical mathematics brain, to be able to convert it into a way which is useful to stakeholders.”From speaking to Nicky, it is clear that Product Analytics is an up-and-coming role that people don’t know enough about it. Therefore, if you are curious about Product Analytics, or any of the different roles the market has to offer at the moment, as an employer looking for help hiring, or a candidate actively or passively looking for work, Harnham can help. Take a look at our latest Product Analytics jobs, or get in touch for more information on how we can support your hiring needs.
Using Data & Analytics to Create the Next Generation of Customer Loyalty Programs | Harnham US Recruitment post
Coupons. Codes. Vouchers. Points. Miles. The list of customer loyalty programs and what they provide is ever-evolving. Or if it isn’t, it should be. While travel and retail are the most well-known industries to offer these programs, other businesses such as healthcare and insurance are coming into the mix as well. Because while everyone knows the adage ‘Know Your Customer’, today, it means more than just their name and what they like to buy. It also means customer service, quality, and a reason to return.Though there are a host of technological advances to help businesses track their customers’ buying journey, behavioral habits, and collect their Data, there is one old-school advertisement to consider. Word-of-Mouth. Consider what and how we review products and services. It’s the digital version of word-of-mouth (read: reviews), Data can transform your customer loyalty programs, if you have the right strategy in place.The Next Generation of Loyalty ProgramsIf you’re just entering the market and want to design your first loyalty program, here are two questions you’ll want to ask yourself before you begin:Why do you want to design a customer loyalty program? If you just want to do what everyone else is doing, it won’t work. If you can’t clearly explain why you want to launch, then you’re not yet ready. Once you can identify the business purpose or objective, then you can think about next steps.How do you define success? What will you use to measure your objectives? Here’s where Advanced Analytics really come into play as you determine which customers you want to target, decide what you want them to do, and ensure your program is flexible enough to grow as your business grows as well as consistent with your messaging across brand channels.Whether emerging or evolving, your business may want to design or relaunch customer loyalty programs. If you’re relaunching because your loyalty program isn’t driving business. Ask yourself why. Options abound in today’s marketplace and the noise of places to buy products is only getting bigger. How you stand out from the rest will be a major driver in the years to come.What Customers WantIn establishing and developing new loyalty programs, it’s essential to begin with the end in mind, and the most important question to drive engagement is this: What do your customers want? Consider these statistics when planning your program, hiring your staff, and developing quality products and services to ensure your customers return again and again. After all, it’s much easier to retain a customer than to acquire a new one.So, while you may know your customer at the surface level and perhaps even a bit deeper through their behaviors and buying habits, Predictive Analytics can also help improve your loyalty program offerings. How? By helping you refine your program to better understand your customers.Want to know what drives your high-value customers? Use Predictive Analytics to determine what you want them to do by understanding your analytics of their buying behaviors and customer journey. You don’t want to give away too much, but you also want to provide value in your offerings and your customer service.Customer loyalty programs are as much about service and product as they are about word-of-mouth and retention. When someone recommends your product on one of their social media channels, they are acting as ambassador for your brand. These are your most loyal customers and what your program will look like in 2022 and in the years to come.If you’re interested in Digital Analytics, Advanced Analytics, or Data Science just to name a few, Harnham may have a role for you. Check out our latest Advanced Analytics jobs or contact one of our expert consultants to learn more. For our West Coast Team, contact us at (415) 614 – 4999 or send an email to firstname.lastname@example.org. For our Arizona Team, contact us at (602) 562 7011 or send an email to email@example.com. For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to firstname.lastname@example.org.
Why Marketing Teams Need to Fill Their Data Skills Gaps
Data can be leveraged in a myriad of ways and be beneficial to numerous business functions.
In marketing, for example, data is playing an increasingly important role in helping brands get closer to their target customers, which ultimately improves the bottom line. Businesses that use data-driven marketing strategies have five times more ROI than those that don’t.
Despite this potential, a new survey has revealed that data analytics is one of the biggest skills gaps in marketing departments. Below, we break down this new research and explain why it’s crucial to fill your company’s data skills gap and build a data-driven marketing team.
So why does this skills gap matter?
The recent research revealed more than a third (34.4%) of the 3,000-plus respondents identified a lack of data analytic skills in their marketing department. For B2B marketers, the figure drops to 29.9 per cent, while it’s 34.6 per cent for B2C marketers, and jumps to 39.6 per cent for businesses with a mix of both.
These findings are particularly pertinent as marketing isn’t a department that operates within a bubble, rather it has its tendrils in every part of an organisation, so when marketing isn’t functioning as optimally as possible, neither is the business.
Businesses that are not harnessing the insights that data analysis offers, are missing out on the ability to understand and meet their customer’s preferences. Making decisions that are not grounded in data means that a business is operating in the dark – throwing ideas at the wall to see what sticks rather than already knowing what will work because the data has told them so.
Many companies have realised that it’s no longer good enough to guess what customers might want or need from a product or service, but to instead have hard evidence to back up these choices. A data-led marketing strategy can revolutionise marketing efforts in numerous ways such as:
Behaviour analysis and personalisation
By analysing a customer’s behaviour, such as their e-commerce and website browsing habits, marketers can ensure that the businesses’ landing pages, calls to action and other marketing tools are working as they should be, and use this data to better tailor content and improve the customer experience.
Behaviour analysis might include examining customer interactions, such as where and when they click on a website, even down to which pages consumers are lingering on for longer. The content you are producing might be incredibly insightful and smart, but that’s irrelevant if customers aren’t reading it. Once you have understood where people do and don’t spend time and which content attracts the most engagement, assets can be shaped to scoop up people who might otherwise leave a site, further entice already interested parties and inform other marketing activities.
For example, if you’re a business that sells clothes, you can use data analytics to determine which colours and styles are most popular among your customers and create content such as fashion tips or trend reports including these colours and styles.
Through monitoring the current behaviour of customers, businesses can also more easily identify when and how their preferences change. For example, if visitors to written pieces are dropping off, you could consider incorporating more video content. Reacting to the subtle changes in customer behaviour can help companies to maintain their position in the market and increase their revenue by tapping into new pools of customers.
Predicting customer patterns
But data isn’t just for making better in-the-moment decisions. It can also help to pre-empt future customer behaviour, allowing businesses to make proactive decisions based on previous trends, rather than acting reactively.
Predictive analytics is the use of data algorithms and techniques to define the likelihood of future events or results, based on historical data from customer habits. It allows marketers to forecast a customer’s “next move”, such as which consumers are most likely to buy again, and therefore prioritise customers.
Based on previous patterns of behaviour, businesses can predict website engagement points where, for example, a customer may convert, but also areas where consumers might lose interest or drop off – friction points such as filling in a form. This information enables businesses to make choices that ensure that the customer experience is as smooth and effective as possible.
How can this skills gap be filled?
The effectiveness of data analysis is dependent on talent being able to carry it out. At Harnham, we specialise in data hires for marketing. In other words, through experience, our consultants have built a comprehensive picture of what marketing teams need when it comes to data marketing talent. When it comes to hiring a data marketing professional there are a wealth of skills to look for, including:
- Being a problem solver – a candidate who can identify logical ways to overcome problems and offer solutions.
- Having a good grounding in coding languages such as SQL. Whilst it can be beneficial to have more advanced modelling skills using R or Python, some companies will have data science teams to support on this side.
- Experience with visualisation tools and with programs such as Tableau or Looker – which can be hugely valuable in hitting the ground running.
Most crucial, however, is the ability to tell a story with the data and make something complex easy to digest. During an interview, businesses can identify how someone translates recommendations and if they are able to recognise and illustrate the commercial impact that their work will have.
If you’re interested in applying your data skills to a role in marketing or are looking to bolster the success of your business by hiring a Data & Analytics specialist, you’ve come to the right place contact our team today.
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