Industry & Market Research
Development of thought leadership work
Explore travel and hospitality research needs in the area of online traffic communities, application of information technology in tourism, big data and analytics, digitalization, and many more when needed utilizing a number of sources and advanced secondary research techniques (desk research, interviews of SMEs, surveys).
Conduct in-depth research on geographic and/or horizontal segment trends, competitors, industry market trends and issues, and relevant technology.
Our expertise has an understanding of quantitative and qualitative research skills, with the ability to synthesize findings from case studies, analysis of survey data, regression analysis, expert interviews etc.
Our analyst team write reports, points of view, articles in journals blogs, develop insightful case studies, design questionnaire, analyse data, runs regressions and time series analysis.
Reliable, Validated Questionnaires
Based on the given source of data, we help you to design a business research question and develop appropriate hypotheses. Along with it, we develop a step-by-step rigorous methodological framework for processing and analysing large data.
We develop standardized questionnaires, interview guides, instruction manuals, and survey tools required for hospitality research. We also design survey programming, host, collect data and manage the survey environment.
Statswork tries to improve the customer engagement and satisfaction by using user data and survey by the customer to gauge the sentiment. We predict the recommendation of the customers and segment them to a particular sector so that they can be able to purchase more in the future and the business goals will be achieved and can boost the ROI.
Consumer packaged goods (CPG) & Retail Organization collect large volume of transactions data while our team of Industry experts helps you to extract these datasets and offer incredible insights to gain better visibility into store conditions.
Nowadays, there are huge amounts of data which is generated from day to day interactions by the customers and employees, those data can be turned into important models which can boost the business ROI. This can be achieved by data annotation and labelling for machine learning model that will produce amazing analytical research on how to engage with customers.
Predicting the purchase behavior of your customer, To personalize in-store experience in order to establish and drive loyalty by giving offers to incentivize frequent consumers, Customer Journey – on every step, your high value customers and their behavior, Analytics on operations and supply chain, Trade promotions optimization
Optimizes sourcing, supply chain management and inventory management through forecasting models, accelerated product innovation by guiding product designs.
Developed models will forecast and predict the demand to optimize and produce inventive decisions to the business management and supply chains.
How people feel about your brand, Which products of your prospective customers are talking about on social media, How many of your customers are satisfied or disappointed / not satisfied or negative comments / experience , In which language as well as the tone with which the products are discussed, How customer feedback data can drive product
Scanner data, invoices and orders, automatic image capture of shelves using image recognition (IR) method to understand how your products are performing at shelf, How many facings do compete brands have & Were your products at the right location on
To recommend products based on customer’s behavior
Past behavior or the series of the products characteristics are under consideration , Demographic data, previous shopping experience, needs, preferences, usefulness – via the past data learning algorithm, Collaborative and content filtering associations are built – up-sell and cross-sell recommendations depends on the analyses
We collect data from website clicks, point of sale systems, mobile apps, supply chain systems, In-store Sensors, CCTV cameras, data from social media (customer posts, profiles on social media)
What SKUs shopper prefer to purchase, the prices they expect, the best location in-store for a product, the optimal display to meet customer needs and what communication consumers’ need and how frequently shopper purchase .
Who’s shopping where? The power of geospatial analytics in omnichannel retail
Sales drivers in every zip code, Area wise spending and segmentation, Primary competitors store within 5 miles, High online spending, High number of wholesale stores…
