Michael Hickins | Content Strategist | July 31, 2023
Data analytics is an increasingly important tool for grocery retailers, helping them understand their customers better and increase the efficiency and profitability of their stores.
First and foremost, analytics provides grocery retailers with valuable insights into customer behavior. By analyzing data on customers, such as purchase history, demographics, and social media activities, grocers can gain a better understanding of what motivates them. Grocers can use this knowledge to tailor marketing strategies and drive sales. For example, a grocery store may use analytics to find out that its most loyal customers are seniors and then create a special promotion targeting them to increase revenues.
Grocers also analyze sales data to identify slow-moving products and adjust their inventory accordingly, helping them minimize costs and maximize profits. Such data analytics is especially important for perishable goods, such as dairy, eggs, and meat. Grocers also use analytics to evaluate checkout processes and identify areas for improvement. For example, a store may use it to determine the optimal number of checkout lanes or to identify areas where cashiers may need additional training.
Finally, by analyzing sales data across different stores, grocery retailers can identify emerging industry trends and changes in customer preferences. Such analyses can inform new product launches and changes to the product mix.
Grocery data analytics is the process of analyzing purchase history, transactions, inventories, and other kinds of raw data—using advanced software applications—to make better marketing, stocking, merchandising, staffing, and other decisions.
For example, analytics software can use algorithms against shopping histories found in customer records to predict the types of goods an online shopper might want to buy in conjunction with an online search they typed. Analytics can also delve into call center logs and alert customer service agents about a problem with a particular product and provide them with a solution or alternative for customers who call about it, perhaps changing the customer’s experience from one of frustration to one of surprise and delight. Grocers often use analytics software combined with historical sales data to make savvier purchasing decisions for their stores, helping them meet customer demand.
The grocery retail business is getting ever more complex and competitive. Grocers must quickly identify new consumer trends, such as increased awareness of food allergies and diets, as well as demand for sustainably grown and organic products—all in the context of price inflation, continued supply disruptions, climate change, and changes in customer behavior in the aftermath of the COVID-19 pandemic.
Data analytics software provides grocers with the tools they need to sift through and correlate reams of empirical data to make better business decisions in a timely manner. Analytics changes the role of data from being merely a way of explaining a grocer’s financial performance in a previous week, month, or quarter to being a tool for predicting the future and prescribing actions to maximize opportunities. For example, data analytics can help grocers detect subtle shifts in consumer purchasing patterns at specific locations—such as a sudden heightened demand for cauliflower pizza at stores near universities—and suggest that those stores carry more of those items than at other locations.
Grocery retailers use data analytics in a cyclical manner, collecting data from both internal and external sources on the status of goods as they arrive in inventory, inventory levels as goods are sold or returned to suppliers, the types of consumers shopping in their stores, and prices charged by competitors. Grocers continually enrich this data and apply analytics to refine their marketing and promotional offers, pricing levels, reorders, returns, and more.
Inventory management is one of the most challenging aspects of the grocery business because of the sheer volume of perishable goods. Data analytics helps grocery retailers ensure that they have sufficient stocks of dairy, meat, fish, and other perishables; it also helps them determine when to pull products off the shelves.
Analytics also helps grocers reduce carrying costs by determining which distribution centers to use and the most efficient trucking routes. In addition, analytics helps grocers manage their inventories of nonfood items and ensure that seasonal goods (such as holiday decorations) are available for display at the right times.
Grocers use data analytics to detect fraud and theft by identifying anomalous customer purchase patterns and tracking inventory levels. For example, analytics based on point-of-sale data can help grocers identify customers making unusually large purchases or frequent returns, which could indicate they’re trying to commit refund fraud. Analytics can also show if a cashier is colluding with a friend or relative to commit fraud by improperly discounting some goods (known as “sweethearting”). Grocers also use data analytics to identify any discrepancies between their actual inventory levels and the inventory recorded in the store's system.
Data analytics tools generate predictive models that can help grocers anticipate when certain foods, beverages, and other items are likely to spoil, enabling them to take action to avoid or minimize the spoilage. Grocers also analyze data to identify areas where spoilage is occurring—such as in transit, with a given logistics company, or within a given area of a particular store—so they can mitigate those occurrences. Additionally, analytics provides grocers with insights into the factors that contribute to spoilage, such as temperature, humidity, and light, dictating changes to how they store and display food items.
Data analytics can help grocers measure the performance of their digital ordering systems. That analysis includes tracking the number of orders processed and how quickly orders are filled, as well as customer satisfaction scores. Additionally, analytics helps grocers identify patterns in online ordering behavior, letting them adjust their processes and offerings accordingly.
Grocery store managers use data analytics to chart their high-traffic times and low-traffic times so they know when to shift their people from low-traffic store functions, such as restocking shelves and cleaning aisles, to higher-traffic functions, such as checking out customers.
They also use analytics to improve inventory management, identifying at a granular level where and when to deploy people to restock specific items—especially important at supercenters and other grocers with very large footprints.
Grocers use loyalty programs to collect data on customers at both physical stores and through online memberships, and they analyze that data to generate personalized coupons and other promotions. These loyalty programs are exceedingly popular with customers—only 11% of customers surveyed say such programs rarely or never influence them. Data analytics also helps grocers correlate affinities to cross-promote goods that are often bought together, particularly by people in the same demographic group. For example, analytics might reveal that people who buy premium coffee also tend to buy organic butter, prodding grocers to group promotions of those high-margin items.
By providing insights into which products are selling well and which aren’t, data analytics helps retailers adjust their inventories and prices accordingly, improving overall operational efficiency. Analytics also helps grocers optimize their staffing levels (see the employee productivity section above) and their store layouts to make the most efficient use of space. For example, reports generated by analytics software can render heat maps showing where customers are lingering the longest. Additionally, grocers can use analytics to identify cost savings opportunities, such as places to reduce waste and energy consumption.
Data analytics can help grocers adjust their prices by providing insights into consumer behavior, pricing trends, and the prices at direct competitors. Analytics also helps grocers understand the impact of discounts and other promotional activities on profits.
Analytics can help grocers gain visibility into their supply chains by providing insights into inventory levels, demand trends, and bottlenecks in supply routes. Analytics can also help grocers identify opportunities to reduce supply chain costs and speed deliveries by optimizing transport routes. Additionally, grocers can analyze data to detect fraud in the supply chain, including noncompliance with product provenance and other regulations.
Grocers use data analytics both on the revenue and cost sides to improve their profitability. By providing insights into customer behavior and preferences, analytics helps grocers develop targeted marketing strategies to increase sales. Analytics also helps grocers identify their most profitable product categories, dictating potential changes to their product mix. On the cost side, analytics can help grocers identify opportunities to improve supply chain efficiency (see section 9) and identify lower-cost suppliers, as well as reduce outlays for energy, labor, materials, and other inputs.
Grocery data analytics software collects, stores, and analyzes industry data. Grocers use it to enhance their understanding of customer behavior, product trends, and purchasing patterns. Grocers also use the software to analyze customer purchase history to identify cross-selling opportunities, and they use it to analyze customer demographics to segment target markets. What’s more, grocery retailers use analytics software to track product sales to determine inventory needs. Other uses include tracking sales performance, analyzing competitors’ pricing strategies, and evaluating promotional effectiveness.
Oracle Retail analytics software provides insights into customer behavior, helping grocers make better decisions about pricing and merchandising. It helps them identify sales trends and opportunities, forecast demand, segment customers into different target groups, and optimize inventory levels. The software also provides visibility into the performance of store operations, helping grocers improve areas such as customer service and store layouts.
Grocers use Oracle Cloud ERP applications to take advantage of Oracle analytics capabilities that help them tailor offers, pricing, and assortments, retain customers through savvy use of loyalty programs, and even allocate the right number of stores per geographic region. The applications also help grocers determine how customers make purchasing decisions, so as to make useful predictions and prescriptive suggestions.
How do grocery stores use big data?
Grocery stores analyze big data to identify customer needs and preferences, adjust their prices, inform promotions, improve customer service, and personalize their offerings. By collecting and analyzing data from a variety of sources, including customer loyalty cards, surveys, websites, point-of-sale (POS) systems, and store-level video, grocers gain insights into customer behaviors and buying patterns. This information helps grocers determine which products to carry, how much of them to stock, and how to best promote and display them.
What data collection methods do supermarkets use?
Supermarkets use a variety of methods and systems to collect data, including online and email surveys, POS systems, loyalty programs, website cookies, video systems, social media analytics, and third-party data providers.
How is data analytics used in retail?
Retailers use data analytics to better understand customer preferences, optimize their pricing and promotions, improve inventory management, analyze the effectiveness of marketing programs and store layouts, and improve customer service.
How do grocery stores use customer transaction data?
Grocery stores use customer transaction data to help them optimize their promotions, identify new product opportunities, develop targeted marketing campaigns, adjust their prices, and manage their inventories.
Michael Hickens, Mkurugenzi wa Mkakati wa Maudhui, Julai 31, 2021 Uchambuzi wa data ni chombo muhimu zaidi kwa wauzaji wa rejareja wa vyakula, kuwasaidia kuelewa wateja wao vizuri na kuongeza ufanisi na faida ya maduka yao. Kwa sababu ya hii, uchambuzi wa bidhaa za rejareja unatoa maoni muhimu juu ya tabia ya wateja. Kwa kuchambua data juu ya wateja, kama vile historia ya ununuzi, idadi ya watu, na shughuli za media ya kijamii, wauzaji wanaweza kupata uelewa bora wa nini kinawachochea. Wafanyabiashara wa vyakula wanaweza kutumia ujuzi huu kurekebisha mikakati ya uuzaji na kuendesha mauzo. Kwa mfano, duka la vyakula linaweza kutumia uchambuzi ili kujua kwamba wateja wake waaminifu zaidi ni wazee na kisha kuunda kukuza maalum kwa lengo la kuongeza mapato. Wafanyabiashara wa vyakula pia huchanganua data za mauzo ili kutambua bidhaa zinazosonga polepole na kurekebisha hesabu yao ipasavyo, kuwasaidia kupunguza gharama na kuongeza faida. Uchambuzi huo wa data ni muhimu hasa kwa bidhaa zinazoweza kuharibika haraka, kama vile maziwa, mayai, na nyama. Wafanyabiashara pia hutumia uchambuzi kutathmini michakato ya malipo na kutambua maeneo ya kuboresha. Kwa mfano, duka linaweza kuitumia kuamua idadi bora ya njia za malipo au kutambua maeneo ambapo wafanyabiashara wa kassa wanaweza kuhitaji mafunzo ya ziada. Mwishowe, kwa kuchambua data ya mauzo katika maduka tofauti, wauzaji wa vyakula wanaweza kutambua mwenendo wa tasnia inayoibuka na mabadiliko katika upendeleo wa wateja. Uchambuzi kama huo unaweza kuelimisha uzinduzi wa bidhaa mpya na mabadiliko ya mchanganyiko wa bidhaa. Uchambuzi wa data ya vyakula ni mchakato wa kuchambua historia ya ununuzi, shughuli, hesabu, na aina nyingine za data mbichi - kwa kutumia programu za juu - kufanya masoko bora, kuhifadhi, biashara, wafanyikazi, na maamuzi mengine. Kwa mfano, programu ya uchambuzi inaweza kutumia algorithms dhidi ya historia ya ununuzi inayopatikana katika rekodi za wateja kutabiri aina za bidhaa ambazo mnunuzi wa mtandaoni anaweza kutaka kununua kwa kushirikiana na utafutaji wa mtandaoni ambao wameandika. Analytics pia inaweza kuchimba katika kumbukumbu za kituo cha simu na kuwaonya mawakala wa huduma ya wateja juu ya tatizo na bidhaa fulani na kuwapa suluhisho au mbadala kwa wateja ambao wanaita juu yake, labda kubadilisha uzoefu wa mteja kutoka kwa moja ya kukata tamaa hadi moja ya mshangao na furaha. Wafanyabiashara wa chakula mara nyingi hutumia programu ya uchambuzi pamoja na data ya mauzo ya kihistoria kufanya maamuzi ya ununuzi wa savvy kwa maduka yao, kuwasaidia kukidhi mahitaji ya wateja. Biashara ya rejareja ya vyakula inakuwa ngumu zaidi na yenye ushindani. "Wauzaji wa vyakula lazima watambue haraka mwenendo mpya wa watumiaji, kama vile kuongezeka kwa ufahamu wa mzio wa chakula na lishe, na pia mahitaji ya bidhaa za kikaboni na za kikaboni, zote katika muktadha wa mfumuko wa bei, usumbufu wa usambazaji unaoendelea, mabadiliko ya hali ya hewa, na mabadiliko katika tabia ya wateja baada ya janga la COVID-19.""" Programu ya uchambuzi wa data hutoa wauzaji wa vyakula na zana wanazohitaji kuchanganua na kuhusisha data nyingi za uzoefu ili kufanya maamuzi bora ya biashara kwa wakati unaofaa. Uchambuzi wa data unabadilisha jukumu la data kutoka kuwa njia tu ya kuelezea utendaji wa kifedha wa duka la vyakula katika wiki, mwezi, au robo iliyopita kuwa chombo cha kutabiri siku zijazo na kuagiza hatua za kuongeza fursa. Kwa mfano, uchambuzi wa data unaweza kusaidia wauzaji wa vyakula kugundua mabadiliko madogo katika mifumo ya ununuzi wa watumiaji katika maeneo maalum - kama vile mahitaji ya ghafla ya pizza ya cauliflower katika maduka karibu na vyuo vikuu - na kupendekeza kwamba maduka hayo hubeba vitu zaidi kuliko katika maeneo mengine. Wauzaji wa rejareja wa vyakula hutumia uchambuzi wa data kwa njia ya mzunguko, kukusanya data kutoka vyanzo vya ndani na vya nje juu ya hali ya bidhaa zinapofika kwenye hifadhi, viwango vya hifadhi wakati bidhaa zinauzwa au kurudishwa kwa wauzaji, aina za watumiaji wanaofanya ununuzi katika maduka yao, na bei zinazotozwa na washindani. Wafanyabiashara wa vyakula huongeza data hii na kutumia uchambuzi ili kuboresha uuzaji wao na matoleo ya uendelezaji, viwango vya bei, maagizo ya upya, kurudi, na zaidi. Usimamizi wa hesabu ni moja ya mambo magumu zaidi ya biashara ya vyakula kwa sababu ya kiasi kikubwa cha bidhaa perishable. Uchambuzi wa data husaidia wauzaji wa vyakula kuhakikisha kuwa wana hifadhi ya kutosha ya maziwa, nyama, samaki, na vitu vingine vinavyoharibika; pia huwasaidia kuamua ni lini kuondoa bidhaa kwenye rafu. Analytics pia husaidia wauzaji kupunguza gharama za kubeba kwa kuamua ni vituo gani vya usambazaji vya kutumia na njia bora zaidi za usafirishaji. Kwa kuongezea, uchambuzi unasaidia wauzaji wa vyakula kusimamia hesabu zao za bidhaa zisizo za chakula na kuhakikisha kuwa bidhaa za msimu (kama mapambo ya likizo) zinapatikana kwa maonyesho wakati unaofaa. Wafanyabiashara wa vyakula hutumia uchambuzi wa data kugundua udanganyifu na wizi kwa kutambua mifumo isiyo ya kawaida ya ununuzi wa wateja na kufuatilia viwango vya hesabu. Kwa mfano, uchambuzi kulingana na data ya hatua ya mauzo unaweza kusaidia wauzaji wa vyakula kutambua wateja wanaofanya ununuzi mkubwa sana au kurudi mara kwa mara, ambayo inaweza kuonyesha wanajaribu kufanya udanganyifu wa kurudisha. Uchambuzi pia unaweza kuonyesha kama cashier ni colluding na rafiki au jamaa kufanya udanganyifu kwa discounting vibaya baadhi ya bidhaa (jina linalojulikana kama <unk>sweethearting<unk>). Wafanyabiashara pia hutumia uchambuzi wa data kutambua kutofautiana yoyote kati ya viwango vyao halisi vya hesabu na hesabu iliyorekodiwa katika mfumo wa duka. Vifaa vya uchambuzi wa data hutengeneza mifano ya utabiri ambayo inaweza kusaidia wauzaji wa vyakula kutabiri wakati vyakula fulani, vinywaji, na vitu vingine vinaweza kuharibika, na kuwawezesha kuchukua hatua ili kuepuka au kupunguza uharibifu. Wafanyabiashara pia kuchambua data kutambua maeneo ambapo uharibifu ni kutokea - kama vile katika usafirishaji, na kampuni fulani ya usafirishaji, au ndani ya eneo fulani la duka fulani - hivyo wanaweza kupunguza matukio hayo. Kwa kuongezea, uchambuzi hutoa wauzaji wa vyakula na ufahamu wa sababu zinazohusika na uharibifu, kama vile joto, unyevu, na mwanga, na kuamua mabadiliko ya jinsi wanavyohifadhi na kuonyesha bidhaa za chakula. Uchambuzi wa data unaweza kusaidia wauzaji wa vyakula kupima utendaji wa mifumo yao ya kuagiza ya dijiti. Uchambuzi huo unatia ndani kufuatilia idadi ya maagizo yaliyoshughulikiwa na jinsi maagizo yanavyojazwa haraka, na pia alama za kuridhika kwa wateja. Kwa kuongezea, uchambuzi unasaidia wauzaji wa vyakula kutambua mifumo katika tabia ya kuagiza mtandaoni, ikiwaruhusu kurekebisha michakato yao na matoleo ipasavyo. Wasimamizi wa maduka ya vyakula hutumia uchambuzi wa data kuorodhesha nyakati zao za trafiki ya juu na nyakati za trafiki ya chini ili waweze kujua wakati wa kuhamisha watu wao kutoka kwa kazi za duka la trafiki ya chini, kama vile kuhifadhi rafu na kusafisha vijia, kwa kazi za trafiki ya juu, kama vile kuangalia wateja. Pia hutumia uchambuzi ili kuboresha usimamizi wa hesabu, kutambua katika ngazi ya granular ambapo na wakati wa kupeleka watu ili kurudisha vitu maalum - muhimu hasa katika supercenters na wauzaji wengine wenye alama kubwa sana. Wafanyabiashara wa bidhaa hutumia programu za uaminifu kukusanya data juu ya wateja katika maduka ya kimwili na kupitia ushirika wa mtandaoni, na huchambua data hiyo ili kuunda kuponi za kibinafsi na matangazo mengine. Programu za uaminifu ni maarufu sana na wateja - 11% tu ya wateja waliohojiwa wanasema programu kama hizo hazina ushawishi wao. Uchambuzi wa data pia husaidia wauzaji wa vyakula kuunganisha uhusiano wa kukuza bidhaa ambazo mara nyingi hununuliwa pamoja, haswa na watu katika kikundi kimoja cha idadi ya watu. Kwa mfano, uchambuzi unaweza kufunua kwamba watu ambao hununua kahawa ya premium pia huwa na kununua siagi ya kikaboni, kuchochea wauzaji wa vyakula kwa kukuza vikundi vya bidhaa hizo zenye faida kubwa. Kwa kutoa ufahamu wa bidhaa ambazo zinauzwa vizuri na ambazo hazina, uchambuzi wa data husaidia wauzaji kurekebisha hesabu zao na bei ipasavyo, kuboresha ufanisi wa jumla wa uendeshaji. Analytics pia husaidia wauzaji wa vyakula kuboresha viwango vyao vya wafanyikazi (tazama sehemu ya uzalishaji wa wafanyikazi hapo juu) na mipangilio yao ya duka ili kutumia nafasi kwa ufanisi zaidi. Kwa mfano, ripoti zinazozalishwa na programu ya uchambuzi zinaweza kutoa ramani za joto zinazoonyesha ambapo wateja wanakaa kwa muda mrefu zaidi. Kwa kuongezea, wauzaji wanaweza kutumia uchambuzi kutambua fursa za kuokoa gharama, kama vile maeneo ya kupunguza taka na matumizi ya nishati. Uchambuzi wa data unaweza kusaidia wauzaji kurekebisha bei zao kwa kutoa ufahamu wa tabia ya watumiaji, mwenendo wa bei, na bei kwa washindani wa moja kwa moja. Analytics pia husaidia wauzaji wa vyakula kuelewa athari za punguzo na shughuli nyingine za uendelezaji kwenye faida. Analytics inaweza kusaidia wauzaji kupata mwonekano katika minyororo yao ya usambazaji kwa kutoa ufahamu wa viwango vya hesabu, mwenendo wa mahitaji, na vizuizi katika njia za usambazaji. Analytics pia inaweza kusaidia wauzaji wa vyakula kutambua fursa za kupunguza gharama za mlolongo wa usambazaji na kuharakisha utoaji kwa kuboresha njia za usafirishaji. Kwa kuongezea, wauzaji wanaweza kuchambua data ili kugundua udanganyifu katika mlolongo wa usambazaji, pamoja na kutofuata chanzo cha bidhaa na kanuni zingine. Wafanyabiashara wa vyakula hutumia uchambuzi wa data kwa upande wa mapato na gharama ili kuboresha faida yao. Kwa kutoa ufahamu wa tabia ya wateja na upendeleo, uchambuzi husaidia wauzaji wa vyakula kuendeleza mikakati ya uuzaji iliyoelekezwa ili kuongeza mauzo. Analytics pia husaidia wauzaji wa vyakula kutambua makundi yao ya bidhaa yenye faida zaidi, kuamuru mabadiliko ya uwezekano wa mchanganyiko wao wa bidhaa. Kwa upande wa gharama, uchambuzi unaweza kusaidia wauzaji wa vyakula kutambua fursa za kuboresha ufanisi wa mlolongo wa usambazaji (tazama sehemu ya 9) na kutambua wauzaji wa gharama nafuu, na pia kupunguza gharama za nishati, kazi, vifaa na viingilio vingine. Programu ya uchambuzi wa data ya maduka ya vyakula hukusanya, kuhifadhi, na kuchambua data ya tasnia. Wafanyabiashara wa vyakula hutumia programu hiyo ili kuelewa vizuri tabia za wateja, mwenendo wa bidhaa, na mifumo ya ununuzi. Wauzaji wa chakula pia hutumia programu hiyo kuchambua historia ya ununuzi wa wateja ili kutambua fursa za kuuza msalaba, na hutumia kuchambua idadi ya watu wa wateja kugawanya masoko ya lengo. Nini zaidi, wauzaji wa rejareja wa vyakula hutumia programu ya uchambuzi kufuatilia mauzo ya bidhaa ili kuamua mahitaji ya hesabu. Matumizi mengine ni pamoja na kufuatilia mauzo ya utendaji, kuchambua washindani'mikakati ya bei, na kutathmini ufanisi wa uendelezaji. Oracle Retail Analytics ni programu ya ujasusi ya Oracle ambayo hutoa ufahamu wa tabia ya wateja, kusaidia wauzaji kufanya maamuzi bora juu ya bei na biashara. Inasaidia kutambua mwenendo wa mauzo na fursa, utabiri wa mahitaji, kugawanya wateja katika vikundi tofauti vya lengo, na kuboresha viwango vya hifadhi. Programu pia hutoa mwonekano katika utendaji wa shughuli za duka, kusaidia wauzaji kuboresha maeneo kama vile huduma ya wateja na mpangilio wa duka. Wafanyabiashara wa Oracle Cloud ERP hutumia uwezo wa Oracle Analytics ambao unasaidia kuandaa matoleo, bei, na aina, kuhifadhi wateja kupitia programu za uaminifu, na hata kugawa idadi sahihi ya maduka kwa kila eneo la kijiografia. Programu hizo pia husaidia wauzaji wa vyakula kuamua jinsi wateja wanavyofanya maamuzi ya ununuzi, ili kufanya utabiri muhimu na mapendekezo ya maagizo. Maduka ya vyakula hutumiaje data kubwa? Maduka ya vyakula yanachambua data kubwa ili kutambua mahitaji na upendeleo wa wateja, kurekebisha bei zao, kutoa matangazo, kuboresha huduma kwa wateja, na kubadilisha matoleo yao. Kwa kukusanya na kuchambua data kutoka vyanzo mbalimbali, ikiwa ni pamoja na kadi za uaminifu wa wateja, tafiti, tovuti, mifumo ya hatua ya mauzo (POS), na video ya kiwango cha duka, wauzaji hupata ufahamu wa tabia za wateja na mifumo ya ununuzi. Habari hii husaidia wauzaji wa vyakula kuamua ni bidhaa zipi za kubeba, ni ngapi za kuhifadhi, na jinsi ya kuzitangaza na kuzionyesha. Ni mbinu gani za kukusanya data zinazotumiwa na maduka makubwa? Maduka makubwa hutumia njia na mifumo mbalimbali ya kukusanya data, ikiwa ni pamoja na tafiti za mtandaoni na barua pepe, mifumo ya POS, programu za uaminifu, kuki za wavuti, mifumo ya video, uchambuzi wa media ya kijamii, na watoa data wa tatu. Utafiti wa data hutumiwaje katika rejareja? Wafanyabiashara wa rejareja hutumia uchambuzi wa data kuelewa vyema upendeleo wa wateja, kuboresha bei na matangazo yao, kuboresha usimamizi wa hesabu, kuchambua ufanisi wa programu za uuzaji na mpangilio wa duka, na kuboresha huduma ya wateja. Maduka ya vyakula hutumiaje data za shughuli za wateja? Maduka ya vyakula hutumia data ya shughuli za wateja kuwasaidia kuboresha matangazo yao, kutambua fursa mpya za bidhaa, kuendeleza kampeni za uuzaji zilizolenga, kurekebisha bei zao, na kusimamia hesabu zao.